nils-herrmann/clean_scirepeval huggingface dataset:
Adaptive early jump-out technique for fast motion estimation in video coding

An adaptive early jump-out technique for speeding up the block-based motion estimation is proposed. By using the new technique, we can speed up the full range search several times without losing the picture quality significantly. The proposed technique can also be embedded into almost all the existing fast motion estimation algorithms to speed up the computation further. Since the proposed technique can be embedded into the existing motion estimation algorithms, it can be applied to almost all the standard video codecs, such as the MPEG coder, and improve the coding speed of such codecs significantly. Our technique has been tested on the H.261 and the MPEG-I codecs, and the coding speed improves significantly.
Efficient post-compression error-resilient 3D-scalable video transmission for packet erasure channels

We propose an efficient error-resilient video transmission algorithm over packet erasure channels using optimal source and channel bit-allocation. This algorithm uses FEC and rate-distortion optimization to find the optimal allocation of source and channel bits in each quality layer. The packet loss probability is periodically reported to the video server. This method can also be incorporated with any coding structure that generates a set of independent compressed bitstreams. The algorithm efficacy is demonstrated by simulations in which the video compression is an error-resilient 3D-SPIHT algorithm and the channel protection is provided by Reed-Solomon (RS) codes.
Distortion estimation and bit allocation for MCTF based 3-D wavelet video coding

In this paper, we propose a novel way to derive the weighting for subbands that conserves energy. Once the weight of each subband has been calculated by motion-compensation temporal filtering, the effect of the quantization error of a subband on the reconstruction error of a group of pictures can be determined. Then, we weigh each subband and use the weighted coefficients to derive the optimal bit-allocation solution for 3-D wavelet coding methods. We apply the proposed method to bit-allocation on a 2D+t structure with a 5-3 temporal wavelet filter, and show that it achieves a 0.5-1.5 dB peak- signal-to-noise improvement over using the weighting of subbands on various coding parameters and sequences.
Advanced motion compensation techniques for blocking artifacts reduction in 3-D video coding systems

This paper describes a new 3-D framework for the construction of a hybrid motion compensation model to reduce blocking artifacts in a highly scalable video coding system. Previous works have focused primarily on using control grid interpolation (CGI) or over-lapped block motion compensation (OBMC) to reduce blocking artifacts. However, both methods generate distinct side effects, such as PSNR degradation or poor de-blocking results, during the de-blocking process. The main objective of our proposed model is to achieve better visual quality and maintain satisfactory coding efficiency simultaneously by integrating the advantages of CGI and OBMC. The introduction of the dual mode selection between CGI and OBMC provides a more flexible mechanism to adjust our objective function and thereby achieves higher objective and subjective coding gains. Experimental results show that our proposed model achieves improved coding efficiency in terms of both PSNR and visual quality.
Timing acquisition for wavelet-based multirate transmissions

The acquisition problem in wavelet-based modulation is very important. We discuss the problem of the ML-based method proposed by M. Luise et al.  2000) for timing acquisition and then we develop a novel acquisition algorithm. Our method uses the properties of the scaling function in the derivation of the acquisition function. We also show that the S-curve of our acquisition algorithm is smooth and has a unique zero during the signaling interval. Thus, we can acquire the correct symbol timing without ambiguity. The performance of our acquisition algorithm is evaluated by Monto-Carlo simulation using the Meyer wavelet.
Biomedical image mosaicing: An optimized multiscale approach

In typical mosaicing or blending algorithms, it is usually assumed that the to-be-combined images are equally informative, and each component image would be processed in a similar manner. However, because of the photobleaching effect, the fluorescence intensity of confocal microscope image may degenerate; therefore, the overlapping regions will become asymmetrically informative. We formulate the problem of mosaicing such images as a multiscale optimization approach. The optimal blending coeffients can thus be obtained by a quadratic programming with constraints enforced by the biological requirements. We also demonstrate the efficacy on several confocal microscope fluorescence images as well as the EM images and compare the mosaicing results with those derived by other methods.
An integral operator based adaptive signal separation approach

The operator-based signal separation approach uses an adaptive operator to separate a signal into additive subcomponents. And different types of operator can depict different properties of a signal. In this paper, we define a new kind of integral operator which can be derived from the second kind of Fredholm integral equation. Then, we analyze the properties of the proposed integral operator and discuss its relation to the second condition of Intrinsic Mode Function (IMF). To demonstrate the robustness and efficacy of the proposed operator, we incorporate it into the Null Space Pursuit algorithm to separate several multicomponent signals, including a real-life signal.
Automatic Microarray Spot Segmentation Using a Snake-Fisher Model

Inspired by Paragious and Deriche's work, which unifies boundary-based and region-based image partition approaches, we integrate the snake model and the Fisher criterion to capture, respectively, the boundary information and region information of microarray images. We then use the proposed algorithm to segment the spots in the microarray images, and compare our results with those obtained by commercial software. Our algorithm is automatic because the parameters are adaptively estimated from the data without human intervention.
Color image enhancement using retinex with robust envelope

In this paper, we propose a color image enhancement method that uses retinex with a robust envelope to improve the visual appearance of an image. The word “retinex” is hybird “retina” and “cortex”, suggesting that human visual perception is involved in this color image enhancement. To avoid the gray-world violation, a color-shifting problem, an input RGB color image is transformed into an HVS color image, but only the V component is enhanced. Furthermore, to prevent hallow artifacts, we construct a robust envelope with a gradient-dependent weighting to limit disturbances around intensity gaps such as edges and corners. Our experiment results show that the proposed method yields a better (almost hallow-free) performance than traditional image enhancement methods.
Texture classification using wavelet scale relationships

It has been documented in the literature that texture can be well characterised by features obtained from its multi-scale representation. Typically, the textured image being analysed is decomposed into separate frequency and/or orientation bands, and features extracted separately from each such band. In this paper, we propose that features modelling the relationships between scale bands of such a representation provide a better characterisation of textured images than features extracted from individual bands alone. Using this conjecture, we develop a novel feature set for texture classification, and demonstrate its effectiveness using a set of images obtained from the Brodatz texture album.
Logarithmic quantisation of wavelet coefficients for improved texture classification performance

The coefficients of the wavelet transform have been widely used for texture analysis tasks, including segmentation, classification and synthesis. Second order statistics of such values have been shown to give excellent performance in these applications, and are typically calculated using co-occurrence matrices, which require quantisation of the coefficients. In this paper, we propose a non-linear quantisation function which is experimentally shown to better characterise textured images, and use this to formulate a new set of texture features, the wavelet log co-occurrence signatures.
Representing and identifying jointed objects using a multiresolution technique

A multiresolution technique for representing and identifying objects in images is presented. Images are pre processed to extract object contours. Extracted contours are parameterised to form 1-D arrays. Object representations are then constructed as sets of contour waveforms viewed at different resolution levels. In the learning phase of the proposed algorithm, a database of object models is constructed which representations of objects obtained form images. The database includes jointed objects defined as those consisting of more than one part. During the object identification phase, object parts are allowed to be moved in the plane perpendicular to the joint's axil. To make the algorithm fast and efficient, only a subset of the representation points is used for object identification. The performance of the proposed algorithm is investigated under shifts, rotations, and scale changes.
On the robustness and security of digital image watermarking

In most of the digital image watermarking schemes, it becomes a common practice to address security in terms of robustness, which is basically a norm in cryptography. Such consideration in developing and evaluation of a watermarking scheme may severely affect the performance and render the scheme ultimately unusable. This paper provides an explicit theoretical analysis towards watermarking security and robustness in figuring out the exact problem status from the literature. With the necessary hypotheses and analyses from technical perspective, we demonstrate the fundamental realization of the problem. Finally, some necessary recommendations are made for complete assessment of watermarking security and robustness.
Detecting Uncommon Trajectories

An effective video surveillance system relies on detection of suspicious activities. In recent times, there has been an increasing focus on detecting anomalies in human behaviour using surveillance cameras as they provide a clue to preventing breaches in security. Human behaviour can be termed as suspicious when it is uncommon in occurrences and deviates from commonly understood behaviour within a particular context. This work aims to detect regions of interest in video sequences based on an understanding of uncommon behaviour. A commonality value is calculated to distinguish between common and uncommon occurrences. The proposed strategy is validated by classifying walking path of the people in a shopping mall corridor. CAVIAR database is used for this purpose. The results demonstrate the efficacy of the proposed approach in detecting deviant walking paths.
Detecting commonly occupied regions in video sequences

An effective video surveillance system relies on the detection of suspicious activities. In recent times, there has been an increasing focus on detecting anomalies in human behaviour using surveillance cameras as they provide a clue to preventing breaches in security. Human behaviour can be termed as suspicious when it is uncommon in occurrence and deviates from commonly understood behaviour within a particular context. This work aims to detect regions of interest in video sequences based on an understanding of uncommon behaviour. A commonality value is calculated to distinguish between common and uncommon occurrences. The proposed strategy is validated by classifying commonly occupied walking path regions in a shopping mall corridor and CAVIAR database is used for this purpose. The results demonstrate the efficacy of the proposed approach in detecting deviant walking paths.
A security system based on human iris identification using wavelet transform

A security system based on the recognition of the iris of human eyes using the wavelet transform is presented. The zero crossings of the wavelet transform are used to extract the unique features obtained from the grey level profiles of the iris. The recognition process is performed in two stages. The first stage consists of building a one dimensional representation of the grey level profiles of the iris followed by obtaining the wavelet transform zero crossings of the resulting representation. The second stage is the matching procedure for iris recognition. The proposed approach uses only a few selected intermediate resolution levels for matching, thus making it computationally efficient as well as less sensitive to noise and quantisation errors. A normalisation process is implemented to compensate for size variations due to the possible changes in the camera to face distance. The technique has been tested on real images in both noise free and noisy conditions. The technique is being investigated for real time implementation, as a standalone system, for access control to high security areas.
A Context-Based Approach for Detecting Suspicious Behaviours

A video surveillance system capable of detecting suspicious activities or behaviours is of paramount importance to law enforcement agencies. Such a system will not only reduce the work load of security personnel involved with monitoring the CCTV video feeds but also improve the time required to respond to any incident. There are two well known models to detect suspicious behaviour: misuse detection models which are dependent on suspicious behaviour definitions and anomaly detection models which measure deviations from defined normal behaviour. However, it is nearly possible to encapsulate the entire spectrum of either suspicious or normal behaviour. One of the ways to overcome this problem is by developing a system which learns in real time and adapts itself to behaviour which can be considered as common and normal or uncommon and suspicious. We present an approach utilising contextual information. Two contextual features, namely, type of behaviour and the commonality level of each type are extracted from long-term observation. Then, a data stream model which treats the incoming data as a continuous stream of information is used to extract these features. We further propose a clustering algorithm which works in conjunction with data stream model. Experiments and comparisons are conducted on the well known CAVIAR datasets to show the efficacy of utilising contextual information for detecting suspicious behaviour. The proposed approach is generic in nature and can be applicable to any features. However for the purpose of this study, we have employed pedestrian trajectories to represent the behaviour of people.
Vision-based pirouettes using radial obstacle profile

Mapping algorithms commonly use "radial sweeps" of the surrounding environment as input. Producing a sweep is a challenging task for a robot using only vision. With no odometers to measure turn angles, a vision-based robot must have another method to verify rotations. In this paper we propose using the radial obstacle profile (ROP), which gives the radial distance to the nearest obstacle in any direction in the robot's field of view. By matching the ROPs before and after a turn, the robot should be able to verify that the expected angle of rotation matches the actual angle. Combining successive ROPs then produces a radial sweep.
Cognitive styles, subject content and the design of computer based instruction

The authors present a study on designing computer based instruction (CBI) considering the individual students' preferred cognitive styles and the effects of the subject contents on the learning outcomes. The bimodal nature of cognitive styles was examined in order to assess the full ramification of cognitive styles on learning. Students' cognitive styles were analysed using cognitive style analysis (CSA) software. On the basis of the CSA results, students used either matched or mismatched CBI material. Analysing test results suggests that certain test tasks may be more suitable to certain cognitive styles than others. Results also support the reported argument that subject types have an affinity to certain cognitive styles and acknowledge the need to consider the nature of the subject matter in designing personalised CBI instruction. The consistent better performance by the matched group suggests potential for further investigations where the limitations cited here may be eliminated. This study was used in teaching some components of a digital communications subject in an electrical engineering course.
Segmentation of bone marrow stromal cells in phase contrast microscopy images

The morphology of bone marrow stromal cells (BMSCs) and the nature of phase contrast images make segmentation of such images challenging as many standard segmentation approaches do not work. This presents an obstacle to the development of systems that could use pattern recognition (PR) techniques to assess culture quality, since successful segmentation is an important precursor to successful pattern recognition. A method is presented for image normalisation and segmentation of cell regions within sub-confluent cell cultures of human bone marrow stromal cells, including a novel method of dealing with the halo associated with phase contrast images. The proposed method was evaluated by measuring its effect on the accuracy of a subsequent PR stage that was trained to discriminate between two BMSC cultures of differing quality. The accuracy achieved averaged 93% across four commonly used PR algorithms, corresponding to an overall accuracy gain of 17% compared to non-normalised, unsegmented images.
Developing a Digital Image Watermarking Model

This paper presents a key based generic model for digital image watermarking. The model aims at addressing an identified gap in the literature by providing a basis for assessing different watermarking requirements in various digital image applications. We start with a formulation of a basic watermarking system, and define system inputs and outputs. We then proceed to incorporate the use of keys in the design of various system components. Using the model, we also define a few fundamental design and evaluation parameters. To demonstrate the significance of the proposed model, we provide an example of how it can be applied to formally define common attacks.
A Multiple-Control Fuzzy Vault

We introduce multiple-control fuzzy vaults allowing generalized threshold, compartmented and multilevel access structure. The presented schemes enable many useful applications employing multiple users and/or multiple locking sets. Introducing the original single control fuzzy vault of Juels and Sudan we identify several similarities and differences between their vault and secret sharing schemes which influence how best to obtain working generalizations. We design multiple-control fuzzy vaults suggesting applications using biometric credentials as locking and unlocking values. Furthermore we assess the security of our obtained generalizations for insider/ outsider attacks and examine the access-complexity for legitimate vault owners.
Object recognition using an affine invariant wavelet representation

A novel algorithm based on the dyadic wavelet transform for recognising a planar object undergoing a general affine transformation is presented. The proposed algorithm has two steps: constructing the representation and the matching process. Two different wavelets associated with a given scaling function are used in the construction. In the matching procedure, only extrema of the representation are used. This makes the process efficient and less sensitive to small variations in the representation. Experimental results show that the representation is robust and, combined with the matching algorithm, it efficiently classifies unknown objects.<<ETX>>
Mobility assessment using simulated Arti.cial Human Vision

Recent research on Artificial Human Vision (AHV, or visual prostheses) has focused on providing visually meaningful information to the blind through electrical stimulation of a visual system component. This paper reports on the use of a programmable PDA-based AHV simulator which can be used by normally sighted participants. Using three different display types, mobility performance on an indoor arti ficial mobility course was assessed using Percentage of Preferred Walking Speed (PPWS) and mobility errors. A looming obstacle alert display was not found to assist with mobility performance. Mobility performance increased as participants learned to use the simulation effectively. Posture, head movements and gait were affected by use of the simulation.
A Scheme for Enhancing Security Using Multiple Fingerprints and the Fuzzy Vault

Enhanced security can be achieved combining biometrics and cryptographic concepts together. Aiming for security enhancement, this paper presents a scheme for merging multiple fingerprints with a cryptographic concept, the fuzzy vault. Thereby multiple fingerprints are eligible to lock and unlock a secret securely embedded within the multiple-control fuzzy vault. Given either threshold, compartmented or multilevel secret sharing access structures, different security aspects can be directed and enhanced. Given the capability of merging multiple biometrics/fingerprints with secret sharing structures implies additionally a major security enhancement. Valuable application scenarios are outlined and security achievements highlighted.
Spherical Diffusion for Scale-Invariant Keypoint Detection in Wide-Angle Images

Two variants of the SIFT algorithm are presented which operate on calibrated central projection wide-angle images characterised as having extreme radial distortion. Both define the scale-space kernel, termed the spherical Gaussian, as the solution of the heat diffusion equation on the unit sphere. Scale-space images are obtained as the convolution of the image mapped to the sphere with the spherical Gaussian which is shift invariant to pure rotation and the radial distortion in the original image. The first method termed sSIFT implements convolution in the spherical Fourier domain, and the second termed pSIFT approximates this process more efficiently in the spatial domain using stereographic projection. Results using real fisheye and equiangular catadioptric image sequences show improvements in the overall matching performance (recall vs 1-precision) of these methods versus SIFT, which treats the image as planar perspective.
A Context Space Model for Detecting Anomalous Behaviour in Video Surveillance

An automatic anomalous human behaviour detection is one of the goals of smart surveillance systems' domain of research. The automatic detection addresses several human factor issues underlying the existing surveillance systems. To create such a detection system, contextual information needs to be considered. This is because context is required in order to understand human behaviour. Unfortunately, the use of contextual information is still limited in the automatic anomalous human behaviour detection approaches. This paper proposes a context space model which has two benefits: (a) It provides guidelines for the system designers to select information which can be used to describe context, (b) It enables a system to distinguish between different contexts. A comparative analysis is conducted between a context-based system which employs the proposed context space model and a system which is implemented based on one of the existing approaches. The comparison is applied on a scenario constructed using video clips from CAVIAR dataset. The results show that the context-based system outperforms the other system. This is because the context space model allows the system to consider knowledge learned from the relevant context only.