nils-herrmann/clean_scirepeval huggingface dataset:
Clustering Through Hybrid Network Architecture With Support Vectors

In this paper, we propose a clustering algorithm based on a two-phased neural network architecture. We combine the strength of an autoencoderlike network for unsupervised representation learning with the discriminative power of a support vector machine (SVM) network for fine-tuning the initial clusters. The first network is referred as prototype encoding network, where the data reconstruction error is minimized in an unsupervised manner. The second phase, i.e., SVM network, endeavors to maximize the margin between cluster boundaries in a supervised way making use of the first output. Both the networks update the cluster centroids successively by establishing a topology preserving scheme like self-organizing map on the latent space of each network. Cluster fine-tuning is accomplished in a network structure by the alternate usage of the encoding part of both the networks. In the experiments, challenging data sets from two popular repositories with different patterns, dimensionality, and the number of clusters are used. The proposed hybrid architecture achieves comparatively better results both visually and analytically than the previous neural network-based approaches available in the literature.
A model for dynamic shape and its applications

Variation in object shape is an important visual cue for deformable object recognition and classification. In this paper, we present an approach to model gradual changes in the 2-D shape of an object. We represent 2-D region shape in terms of the spatial frequency content of the region contour using Fourier coefficients. The temporal changes in these coefficients are used as the temporal signatures of the shape changes. Specifically, we use autoregressive model of the coefficient series. We demonstrate the efficacy of the model on several applications. First, we use the model parameters as discriminating features for object recognition and classification. Second, we show the use of the model for synthesis of dynamic shape using the model learned from a given image sequence. Third, we show that, with its capability of predicting shape, the model can be used to predict contours of moving regions, which can be used as initial estimates for the contour, based tracking methods.
A topological and temporal correlator network for spatiotemporal pattern learning, recognition, and recall

In this paper, we describe the design of an artificial neural network for spatiotemporal pattern recognition and recall. This network has a five-layered architecture and operates in two modes: pattern learning and recognition mode, and pattern recall mode. In pattern learning and recognition mode, the network extracts a set of topologically and temporally correlated features from each spatiotemporal input pattern based on a variation of Kohonen's self-organizing maps. These features are then used to classify the input into categories based on the fuzzy ART network. In the pattern recall mode, the network can reconstruct any of the learned categories when the appropriate category node is excited or probed. The network performance was evaluated via computer simulations of time-varying, two-dimensional and three-dimensional data. The results show that the network is capable of both recognition and recall of spatiotemporal data in an on-line and self-organized fashion. The network can also classify repeated events in the spatiotemporal input and is robust to noise in the input such as distortions in the spatial and temporal content.
Extracting a fluid dynamic texture and the background from video

Given the video of a still background occluded by a fluid dynamic texture (FDT), this paper addresses the problem of separating the video sequence into its two constituent layers. One layer corresponds to the video of the unoccluded background, and the other to that of the dynamic texture, as it would appear if viewed against a black background. The model of the dynamic texture is unknown except that it represents fluid flow. We present an approach that uses the image motion information to simultaneously obtain a model of the dynamic texture and separate it from the background which is required to be still. Previous methods have considered occluding layers whose dynamics follows simple motion models (e.g. periodic or 2D parametric motion). FDTs considered in this paper exhibit complex stochastic motion. We consider videos showing an FDT layer (e.g. pummeling smoke or heavy rain) in front of a static background layer (e.g. brick building). We propose a novel method for simultaneously separating these two layers and learning a model for the FDT. Due to the fluid nature of the DT, we are required to learn a model for both the spatial appearance and the temporal variations (due to changes in density) of the FDT, along with a valid estimate of the background. We model the frames of a sequence as being produced by a continuous HMM, characterized by transition probabilities based on the Navier-Stokes equations for fluid dynamics, and by generation probabilities based on the convex matting of the FDT with the background. We learn the FDT appearance, the FDT temporal variations, and the background by maximizing their joint probability using interactive conditional modes (ICM). Since the learned model is generative, it can be used to synthesize new videos with different backgrounds and density variations. Experiments on videos that we compiled demonstrate the performance of our method.
Segmentation based denoising using multiple compaction domains

In this paper, we propose a novel segmentation based denoising algorithm. Segmentation yields intrinsically homogeneous and extrinsically heterogeneous regions. A denoising algorithm that uses Multiple Compaction Domains (MCD) is then applied on each of the resulting segments. Such a scheme retains important perceptual information in the segment boundaries while the denoising algorithm operates only on homogeneous segments. Further, the MCD algorithm is demonstrably superior to the classical denoising algorithms using transform domain thresholding. Our algorithm yields better perceptual quality and superior PSNR as compared to MATLAB's adaptive Wiener filter.
Towards structure and motion estimation from dynamic silhouettes

Addresses the problem of estimating the structure and motion of a smooth curved object from its silhouettes observed over time by a trinocular imagery. We first construct a model for the local structure along the silhouette for each frame in the temporal sequence. The local models are then integrated into a global surface description by estimating the motion between successive frames. The algorithm tracks certain surface and image features (parabolic points and silhouette inflections, frontier points) which are used to bootstrap the motion estimation process. The whole silhouette is then used to refine the initial motion estimate. We have implemented the proposed approach and report preliminary results.<<ETX>>
Low-Level Image Segmentation Based Scene Classification

This paper is aimed at evaluating the semantic information content of multiscale, low-level image segmentation. As a method of doing this, we use selected features of segmentation for semantic classification of real images. To estimate the relative measure of the information content of our features, we compare the results of classifications we obtain using them with those obtained by others using the commonly used patch/grid based features. To classify an image using segmentation based features, we model the image in terms of a probability density function, a Gaussian mixture model (GMM) to be specific, of its region features. This GMM is fit to the image by adapting a universal GMM which is estimated so it fits all images. Adaptation is done using a maximum-aposteriori criterion. We use kernelized versions of Bhattacharyya distance to measure the similarity between two GMMs and support vector machines to perform classification. We outperform previously reported results on a publicly available scene classification dataset. These results suggest further experimentation in evaluating the promise of low level segmentation in image classification.
Sparse Coding of Linear Dynamical Systems with an Application to Dynamic Texture Recognition

Given a sequence of observable features of a linear dynamical system (LDS), we propose the problem of finding a representation of the LDS which is sparse in terms of a given dictionary of LDSs. Since LDSs do not belong to Euclidean space, traditional sparse coding techniques do not apply. We propose a probabilistic framework and an efficient MAP algorithm to learn this sparse code. Since dynamic textures (DTs) can be modeled as LDSs, we validate our framework and algorithm by applying them to the problems of DT representation and DT recognition. In the case of occlusion, we show that this sparse coding scheme outperforms conventional DT recognition methods.
3D Texture Classification Using the Belief Net of a Segmentation Tree

This paper presents a statistical approach to 3D texture classification from a single image obtained under unknown viewpoint and illumination. Unlike in prior work, in which texture primitives (textons) are defined in a filter-response space, and texture classes modeled by frequency histograms of these textons, we seek to extract and model geometric and photometric properties of image regions defining the texture. To this end, texture images are first segmented by a multiscale segmentation algorithm, and a universal set of texture primitives is specified over all texture classes in the domain of region geometric and photometric properties. Then, for each class, a tree-structured belief network (TSBN) is learned, where nodes represent the corresponding image regions, and edges, their statistical dependencies. A given unknown texture is classified with respect to the maximum posterior distribution of the TSBN. Experimental results on the benchmark CUReT database demonstrate that our approach outperforms the state-of-the-art methods
Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution

Convolutional neural networks have recently demonstrated high-quality reconstruction for single-image super-resolution. In this paper, we propose the Laplacian Pyramid Super-Resolution Network (LapSRN) to progressively reconstruct the sub-band residuals of high-resolution images. At each pyramid level, our model takes coarse-resolution feature maps as input, predicts the high-frequency residuals, and uses transposed convolutions for upsampling to the finer level. Our method does not require the bicubic interpolation as the pre-processing step and thus dramatically reduces the computational complexity. We train the proposed LapSRN with deep supervision using a robust Charbonnier loss function and achieve high-quality reconstruction. Furthermore, our network generates multi-scale predictions in one feed-forward pass through the progressive reconstruction, thereby facilitates resource-aware applications. Extensive quantitative and qualitative evaluations on benchmark datasets show that the proposed algorithm performs favorably against the state-of-the-art methods in terms of speed and accuracy.
Phase PCA for Dynamic Texture Video Compression

Temporal or dynamic textures (DT's) are video sequences that are spatially repetitive and temporally stationary. DT's are temporal analogs of the well known spatial still image texture. Examples of DT's include moving water, foliage, smoke, clouds, etc. We present a new DT model that can efficiently compress DT sequences. Our proposed method compactly represents the spatiotemporal properties of a DT by modelling its varying Fourier phase content, which can be shown to be the major determinant of both its dynamics and appearance. This is possible because this method combines both temporal and spatial properties in a compact spectral framework. Making use of the benefits inherent to working in the frequency domain, this model provides a significant improvement in DT compression, which can be used to improve the performance of MPEG-2 encoding. We will present experimental evidence that validates this method for a variety of complex sequences, while also comparing it to the most recent DT representational model that is based on modelling a DT as a linear dynamical system (LDS).
NETRA: A Hierarchical and Partitionable Architecture for Computer Vision Systems

Computer vision is regarded as one of the most complex and computationally intensive problems. In general, a Computer Vision System (CVS) attempts to relate scene(s) in terms of model(s). A typical CVS employs algorithms from a very broad spectrum such as numerical, image processing, graph algorithms, symbolic processing, and artificial intelligence. The authors present a multiprocessor architecture, called "NETRA," for computer vision systems. NETRA is a highly flexible architecture. The topology of NETRA is recursively defined, and hence, is easily scalable from small to large systems. It is a hierarchical architecture with a tree-type control hierarchy. Its leaf nodes consists of a cluster of processors connected with a programmable crossbar with selective broadcast capability to provide the desired flexibility. The processors in clusters can operate in SIMD-, MIMD- or Systolic-like modes. Other features of the architecture include integration of limited data-driven computation within a primarily control flow mechanism, block-level control and data flow, decentralization of memory management functions, and hierarchical load balancing and scheduling capabilities. The paper also presents a qualitative evaluation and preliminary performance results of a cluster of NETRA.
Vision based fire detection

Vision based fire detection is potentially a useful technique. With the increase in the number of surveillance cameras being installed, a vision based fire detection capability can be incorporated in existing surveillance systems at relatively low additional cost. Vision based fire detection offers advantages over the traditional methods. It will thus complement the existing devices. In this paper, we present spectral, spatial and temporal models of fire regions in visual image sequences. The spectral model is represented in terms of the color probability density of fire pixels. The spatial model captures the spatial structure within a fire region. The shape of a fire region is represented in terms of the spatial frequency content of the region contour using its Fourier coefficients. The temporal changes in these coefficients are used as the temporal signatures of the fire region. Specifically, an auto regressive model of the Fourier coefficient series is used. Experiments with a large number of scenes show that our method is capable of detecting fire reliably.
Motion and Structure From Two Perspective Views: Algorithms, Error Analysis, and Error Estimation

Deals with estimating motion parameters and the structure of the scene from point (or feature) correspondences between two perspective views. An algorithm is presented that gives a closed-form solution for motion parameters and the structure of the scene. The algorithm utilizes redundancy in the data to obtain more reliable estimates in the presence of noise. An approach is introduced to estimating the errors in the motion parameters computed by the algorithm. Specifically, standard deviation of the error is estimated in terms of the variance of the errors in the image coordinates of the corresponding points. The estimated errors indicate the reliability of the solution as well as any degeneracy or near degeneracy that causes the failure of the motion estimation algorithm. The presented approach to error estimation applies to a wide variety of problems that involve least-squares optimization or pseudoinverse. Finally the relationships between errors and the parameters of motion and imaging system are analyzed. The results of the analysis show, among other things, that the errors are very sensitive to the translation direction and the range of field view. Simulations are conducted to demonstrate the performance of the algorithms and error estimation as well as the relationships between the errors and the parameters of motion and imaging systems. The algorithms are tested on images of real-world scenes with point of correspondences computed automatically.
Motion-based background subtraction and panoramic mosaicing for freight train analysis

We propose a new motion-based background removal technique which along with panoramic mosaicing forms the core of a vision system we have developed for analyzing the loading efficiency of intermodal freight trains. This analysis is critical for estimating the aerodynamic drag caused by air gaps present between loads in freight trains. The novelty of our background removal technique lies in using conventional motion estimates to design a cost function which can handle challenging textureless background regions, e.g. clear blue sky. Supplemented with domain knowledge, we have built a system which has outperformed some recent background removal methods applied to our problem. We also build an orthographic mosaic of the freight train allowing identification of load types and gap lengths between them. The complete system has been installed near Sibley, Missouri, US and processes about 20-30 (5-10 GB/train video data depending on train length) trains per day with high accuracy.
Multiresolution image acquisition and surface reconstruction

Consideration is given to the problem of surface reconstruction from stereo images for large scenes having large depth ranges where it is necessary to aim cameras in different directions and to fixate at different objects. The authors concentrate on the selection of new fixation points from among the nonfixated, low resolution scene parts, and subsequent processing for surface reconstruction. The coarse stereo estimates in the vicinity of the new fixation point are refined as the images of the new fixation point gradually deblur during the process of refixation and are subsequently used to analyze the fixated parts of the scene.<<ETX>>
Segmentation based reversible image compression

Reversible compression of images has been the topic of considerable research as it finds applications in many fields in which the deviation of the reproduced image from the original image is intolerable, however small be the deviation. This paper is concerned with the problem of reducing spatial redundancies in gray scale images, thus providing effective lossless compression, using segmentation information. We will present new edge models that deal effectively with two issues that make such models normally unsuitable for compression applications: local applicability and large number of parameters needed for representation. Segmentation information is provided by a recent transform (1993), which we found to possess qualities making it especially suitable for compression. The final residual image is obtained using autocorrelation-based 2-D linear prediction. Different implementations providing lossless compression are presented along with results over a number of common test images. Results show that the proposed approach can be used to yield robust lossless compression, while providing consistently and significantly better results than the best possible JPEG lossless coder.
Shadow Removal Using Bilateral Filtering

In this paper, we propose a simple but effective shadow removal method using a single input image. We first derive a 2-D intrinsic image from a single RGB camera image based solely on colors, particularly chromaticity. We next present a method to recover a 3-D intrinsic image based on bilateral filtering and the 2-D intrinsic image. The luminance contrast in regions with similar surface reflectance due to geometry and illumination variances is effectively reduced in the derived 3-D intrinsic image, while the contrast in regions with different surface reflectance is preserved. However, the intrinsic image contains incorrect luminance values. To obtain the correct luminance, we decompose the input RGB image and the intrinsic image. Each image is decomposed into a base layer and a detail layer. We obtain a shadow-free image by combining the base layer from the input RGB image and the detail layer from the intrinsic image such that the details of the intrinsic image are transferred to the input RGB image from which the correct luminance values can be obtained. Unlike previous methods, the presented technique is fully automatic and does not require shadow detection.
Efficient collision detection among objects in arbitrary motion using multiple shape representations

We propose an efficient method for detecting potential collisions among multiple objects with arbitrary motion (translation and rotation) in 3D space. The method is useful for online monitoring and path planning in a 3D environment in which there are multiple independently-moving objects. The method consists of two main stages: 1) the coarse stage, an approximate test is performed to identify interfering objects in the entire workspace using octree representation of object shapes; and 2) the fine stage, polyhedral representation of object shapes is used to more accurately identify any object parts that might cause interference and collisions. For this purpose, specific pairs of faces belonging to any of the interfering objects found in the first stage are tested, thus performing detailed computation on a reduced amount of data. Experimental results, which demonstrate the efficiency of the proposed collision detection method, are given.
Segmentation-based Perceptual Image Quality Assessment (SPIQA)

Computational representation of perceived image quality is a fundamental problem in computer vision and image processing, which has assumed increased importance with the growing role of images and video in human-computer interaction. It is well-known that the commonly used Peak Signal-to-noise ratio (PSNR), although analysis-friendly, falls far short of this need. We propose a perceptual image quality measure (IQM) in terms of an image's region structure. Given a reference image and its "distorted" version, we propose a "full-reference" IQM, called segmentation-based perceptual image quality as sessment (SPIQA), which quantifies this quality reduction, while minimizing the disparity between human judgment and automated prediction of image quality. One novel feature of SPIQA is that it enables the use of inter- and intra- region attributes in a way that closely resembles how the human visual system (HVS) perceives distortion. Experimental results over a number of images and distortion types demonstrate SPIQA's performance benefits.
Estimating sensor orientation in cameras

For most imaging cameras, it is desirable that the sensor plane be perpendicular to the optical axis. Such an orientation ensures that the imaging configuration is perspective and planar scene objects perpendicular to the optical axis can be focussed in their entirety. In this paper, we present an image processing method to estimate and subsequently correct the sensor tilt with precision. We propose to measure the tilt by measuring the variation of defocus in an image of a planar calibration chart placed perpendicular to the optical axis. We show that the proposed defocusing based method is inherently more accurate than geometry based techniques which estimate tilt by measuring the deviation in the geometry of a scene pattern. We analyze the sensitivity of the tilt estimates to errors in the experimental setup and show that the proposed technique is quite robust to errors even as large as 1 degree in the orientation of the calibration chart.