nils-herrmann/clean_scirepeval huggingface dataset:
Classification based on symmetric maximized minimal distance in subspace (SMMS)

We introduce a new classification algorithm based on the concept of symmetric maximized minimal distance in subspace (SMMS). Given the training data of authentic samples and imposter samples in the feature space, SMMS tries to identify a subspace in which all the authentic samples are close to each other and all the imposter samples are far away from the authentic samples. The optimality of the subspace is determined by maximizing the minimal distance between the authentic samples and the imposter samples in the subspace. We present a procedure to achieve such optimality and to identify the decision boundary. The verification procedure is simple since we only need to project the test sample to the subspace and compare it against the decision boundary. Using face authentication as an example, we show that the proposed algorithm outperforms several other algorithms based on support vector machines (SVM).
Mean shift feature space warping for relevance feedback

Relevance feedback has been taken as an essential tool to enhance content-based information retrieval systems by keeping the user in the retrieval loop. Among the fundamental relevance feedback approaches, feature space warping has been proposed as an effective approach for bridging the gap between high-level semantics and the low-level features. By examining the fundamental behavior of the feature space warping, we propose a new approach to harness its strength and resolve its weakness under various data distributions. Experiments on both synthetic data and real data reveal significant improvement from the proposed method.
Vector valued sigma delta modulators for color image applications

A vector valued /spl Sigma//spl Delta/ modulator is proposed for multi-input signal applications, such as color imaging. Traditional /spl Sigma//spl Delta/ modulators can be modeled with a vector quantizer in 1-D signal space. The proposed /spl Sigma//spl Delta/ modulator extends this concept to M-D vector space. An optimal noise shaping filter for the oversampled vector-valued /spl Sigma//spl Delta/ modulator is considered. Simulation results verified the improvement provided by the proposed modulator.
A statistical framework for image-based relighting

With image-based relighting (IBL), one can render realistic relit images of a scene without prior knowledge of object geometry in the scene. However, traditional IBL methods require a large number of basis images, each corresponding to a lighting pattern, to estimate the surface reflectance function (SRF) of the scene. We present a statistical approach to estimating the SRF which requires fewer basis images. We formulate the SRF estimation problem in a signal reconstruction framework. We use principal component analysis (PCA) (Duda, R.O. et al., "Pattern Classification, 2nd edition", p.115-17, Wiley Interscience, 2000) to show that the most effective lighting patterns for the data acquisition process are the SRF covariance matrix eigenvectors corresponding to the largest eigenvalues. In addition, we show that, for typical SRFs, especially when the objects have Lambertian surfaces, DCT-based lighting patterns perform as well as the optimal PCA-based lighting patterns. We compare SRF estimation performance of the statistical approach with traditional IBL techniques. Experimental results show that the statistical approach can achieve better performance with fewer basis images.
A Topic-Motion Model for Unsupervised Video Object Discovery

The bag-of-words representation has attracted a lot of attention recently in the field of object recognition. Based on the bag-of-words representation, topic models such as probabilistic latent semantic analysis (PLSA) have been applied to unsupervised object discovery in still images. In this paper, we extend topic models from still images to motion videos with the integration of a temporal model. We propose a novel spatial-temporal framework that uses topic models for appearance modeling, and the probabilistic data association (PDA) filter for motion modeling. The spatial and temporal models are tightly integrated so that motion ambiguities can be resolved by appearance, and appearance ambiguities can be resolved by motion. We show promising results that cannot be achieved by appearance or motion modeling alone.
Multidimensional multirate filters and filter banks derived from one-dimensional filters

A method by which every multidimensional (M-D) filter with an arbitrary parallelepiped-shaped passband support can be designed and implemented efficiently is presented. It is shown that all such filters can be designed starting from an appropriate one-dimensional prototype filter and performing a simple transformation. With D denoting the number of dimensions, the complexity of design and implementation of the M-D filter are reduced from O(N/sup D/) to O(N). Using the polyphase technique, an implementation with complexity of only 2N is obtained in the two-dimensional. Even though the filters designed are in general nonseparable, they have separable polyphase components. One special application of this method is in M-D multirate signal processing, where filters with parallelepiped-shaped passbands are used in decimation, interpolation, and filter banks. Some generalizations and other applications of this approach, including M-D uniform discrete Fourier transform (DFT) quadrature mirror filter banks that achieve perfect reconstruction, are studied. Several design example are given.
Gesture-enhanced information retrieval and presentation in a distributed learning environment

We describe a novel multimodal gesture-enhanced interface to provide immersive as well as non-immersive, non-intrusive and natural interaction between the user and the information system in a distributed learning environment. Different types of gestures are dynamically transformed into clues for spatial/temporal queries for multimedia information sources and databases. The transformation can depend upon the location, the time, the task, and the user profile. Using various transcoding schemes, the retrieved information can be presented to the user in a multi-resolution, multi-dimensional, and multimodal manner.
Investigating useful and distinguishing features around the eyelash region

Traditionally, iris recognition is always about analyzing and extracting features from iris texture. We proposed to investigate regions around eyelashes and extract useful information which helps us to perform ethnic classification. We propose an algorithm which is easy to implement and effective. First, we locate eyelash region by using ASM to model eyelid boundary. Second, we extract local patch around local landmarks. After image processing, we are able to separate eyelashes and extract features from the directions of eyelashes. Those features are descriptive and can be used to train classifiers. Experimental results show our method can successfully perform East-Asian/Caucasian classification up to 93% accuracy, which shows our proposed method is useful and promising.
Biometrics-based cryptographic key generation

Instead of using PINs and passwords as cryptographic keys that are either easy to forget or vulnerable to dictionary attacks, easy-to-carry and difficult-to-transfer keys can be generated based on user-specific biometric information. A framework is proposed to generate stable cryptographic keys from biometric data that is unstable in nature. The proposed framework differs from prior work in that user-dependent transforms are utilized to generate more compact and distinguishable features. Thereby, a longer and more stable bitstream can be generated as the cryptographic key. Experiments are performed on a face database to verify the feasibility of the proposed framework. The preliminary result is very encouraging.
Toward Holistic Scene Understanding: Feedback Enabled Cascaded Classification Models

Scene understanding includes many related subtasks, such as scene categorization, depth estimation, object detection, etc. Each of these subtasks is often notoriously hard, and state-of-the-art classifiers already exist for many of them. These classifiers operate on the same raw image and provide correlated outputs. It is desirable to have an algorithm that can capture such correlation without requiring any changes to the inner workings of any classifier. We propose Feedback Enabled Cascaded Classification Models (FE-CCM), that jointly optimizes all the subtasks while requiring only a “black box” interface to the original classifier for each subtask. We use a two-layer cascade of classifiers, which are repeated instantiations of the original ones, with the output of the first layer fed into the second layer as input. Our training method involves a feedback step that allows later classifiers to provide earlier classifiers information about which error modes to focus on. We show that our method significantly improves performance in all the subtasks in the domain of scene understanding, where we consider depth estimation, scene categorization, event categorization, object detection, geometric labeling, and saliency detection. Our method also improves performance in two robotic applications: an object-grasping robot and an object-finding robot.
High Performance Stereo Vision Designed for Massively Data Parallel Platforms

Real-time stereo vision is attractive in many applications like robot navigation and 3-D scene reconstruction. Data parallel platforms, e.g., graphics processing unit (GPU), are often used for real-time stereo, because most stereo algorithms involve a large portion of data parallel computations. In this paper, we propose a stereo system on GPU which pushes the Pareto-efficiency frontline in the accuracy and speed tradeoff space. Our system is based on a hardware-aware algorithm design approach. The system consists of new algorithms and code optimization techniques. We emphasize on keeping the highly data parallel structure in the algorithm design process such that the algorithms can be effectively mapped to massively data parallel platforms. We propose two stereo algorithms: namely, exponential step size adaptive weight (ESAW), and exponential step size message propagation (ESMP). ESAW reduces computational complexity without sacrificing disparity accuracy. ESMP is an extension of ESAW, which incorporates the smoothness term to better model non-frontal planes. ESMP offers additional choice in the accuracy and speed tradeoff space. We adopt code optimization methodologies from the performance tuning community, and apply them to this specific application. Such an approach gives higher performance than optimizing the code in an “ad hoc” manner, and helps understanding the code efficiency. Experiment results demonstrate a speedup factor of 2.7-8.5 over state-of-the-art stereo systems at comparable disparity accuracy.
I/P frame selection using classification based mode decision

There are many mode decisions in the video coding process that are used to optimize the performance in terms of the bit rate, the speed and the quality of the decoded video. We describe a classification based scheme for making mode decisions in the video coding process. We then illustrate the performance of the scheme using I/P frame selection as an example. The performance of our scheme is measured in terms of both the bit rate and the computation complexity, across different kinds of sequences, and the results are very encouraging.
Classification-Error Cost Minimization Strategy: DCMS

Several classification applications such as intrusion detection, biometric recognition, etc. have different costs associated with different classification errors. In such scenarios, the goal is to minimize the cost incurred, and not the classification error rate itself. This paper proposes a Cost Minimization Strategy, dCMS, which when applied to classifiers, provides a boost in the performance by reducing the cost incurred due to classification errors. dCMS is classifier-type independent, however it exploits the statistical properties of the trained classifier. It does not require classifiers to be retrained, which is particularly advantageous in scenarios where the costs vary dynamically. Convincing results are provided which indicate the statistically significant reduction in cost incurred by applying dCMS, in a diverse set of classification scenarios with datasets and classifiers of varying complexities.
General theory of time-reversed inversion for perfect reconstruction filter banks

It is shown how to invert a MIMO system with the transfer matrix E(z). Assuming that (det E(z)) does not have any zeros on the unit circle the time-reversal technique can be used to obtain a stable inverse system. Due to the time-reversal, proper initial conditions have to be chosen so that perfect reconstruction can be achieved. Using the state-space descriptions of linear systems, it is shown that the initial condition that guarantees perfect recovery is in fact the final condition of the original system. One important application of the technique is that for any analysis filters in an analysis/synthesis filter bank, a stable implementation of the synthesis filter bank can always be derived. Hence, constraints such as FIR filters and the paraunitary property are not necessary to assure stability of the synthesis filters.<<ETX>>
A framework of extracting multi-scale features using multiple convolutional neural networks

Most works related to convolutional neural networks (CNN) use the traditional CNN framework which extracts features in only one scale. We propose multi-scale convolutional neural networks (MSCNN) which can not only extract multi-scale features but also solve the issues of the previous methods which use CNN to extract multi-scale features. With the assumption of label-inheritable (LI) property, we also propose a method to generate exponentially more training examples for MSCNN from the given training set. Our experimental results show that MSCNN outperforms both the state-of-the-art methods and the traditional CNN framework on artist, artistic style, and architectural style classification, supporting that MSCNN outperforms the traditional CNN framework on the tasks which at least partially satisfy LI property.
Video coding with adaptive block update

The encoding mode selection issue in a multimode block based video coder is addressed. An adaptive strategy is proposed which exploits interframe correlation to achieve low bit rate coding while maintaining the image quality. Interframe intensity matching is proposed to minimize 'Intensity Blocking' in selecting the encoding mode. Further bit rate reduction is achieved by nonuniform weight assignment in different encoding modes. The proposed strategy has been integrated with the ITU-T H.263 video coding standard. Promising results in reducing the bit rate and blocking artifacts are obtained when compared with the test model (TMN 6) of H.263.
Cameras and gravity: Estimating planar object orientation

Photography on a mobile camera provides access to additional sensors. In this paper, we estimate the absolute orientation of a planar object with respect to the ground, which can be a valuable prior for many vision tasks. To find the planar object orientation, our novel algorithm combines information from a gravity sensor with a planar homography that matches a region of an image to a training image (e.g., of a company logo). We demonstrate our approach with an iPhone application that records the gravity direction for each captured image. We find a homography that maps the training image to the test image, and propose a novel homography decomposition to extract the rotation matrix. We believe this is the first paper to estimate absolute planar object orientation by combining the inertial sensor information with vision algorithms. Experiments show that our proposed algorithm performs reliably.
Cutout-search: Putting a name to the picture

We often come across photographs with content whose identity we can no longer recall. For instance, we may have a picture from a football game we went to, but do not remember the name of the team in the photograph. A natural instinct may be to query an image search engine with related general terms, such as `football' or `football teams' in this case. This would lead to many irrelevant retrievals, and the user would have to manually examine several pages of retrieval results before he can hope to find other images containing the same team players and look at the text associated with these images to identify the team. With the growing popularity of global image matching techniques, one may consider matching the query image to other images on the Web. However, this does not allow for ways to focus on the object-of-interest while matching, and may cause the background to overwhelm the matching results, especially when the object-of-interest is small and can occur in varying backgrounds, again, leading to irrelevant retrievals. We propose Cutout-Search, where a user employs an interactive segmentation tool to cut out the object-of-interest from the image, and use this Cutout-Query to retrieve images. As our experiments show, this leads to retrieval of more relevant images when compared to global image matching leading to more specific identification of the object-of-interest in the query image.
Semantic-Shift for Unsupervised Object Detection

The bag of visual words representation has attracted a lot of attention in the computer vision community. In particular, Probabilistic Latent Semantic Analysis (PLSA) has been applied to object recognition as an unsupervised technique built on top of the bag of visual words representation. PLSA, however, does not explicitly consider the spatial information of the visual words. In this paper, we propose an iterative technique, where a modified form of PLSA provides location and scale estimates of the foreground object through the estimated latent semantic. In return, the updated location and scale estimates will improve the estimate of the latent semantic. We call this iterative algorithm Semantic-Shift. We show results with significant improvements over PLSA.
Estimation and mode decision for spatially correlated motion sequences

There are several parts of the video-encoding process that can be optimized for high quality, high compression ratio, and fast encoding speed. We focus on some of these encoder optimization issues. We first study motion estimation, a computationally intensive part of encoding, and propose efficient algorithms based on exploiting spatially correlated motion information. We extend our motion estimation algorithms to find motion vectors for sub-parts of a block, as allowed for in the H.263 and MPEG-4 standards, and propose an efficient two-tier decision strategy for the one-four motion-vector decision. We introduce a new measure of block similarity that can improve the inter-intra mode decision significantly. Furthermore, for certain motion-estimation strategies, this new measure can also improve the bit rate resulting from a better motion estimation. Another issue that we address is the estimation of delta motion vectors in the H.263 and MPEG-4 standards. We have implemented these optimizations in a H.263 framework and the results are encouraging.
A Subspace Model-Based Approach to Face Relighting Under Unknown Lighting and Poses

We present a new approach to face relighting by jointly estimating the pose, reflectance functions, and lighting from as few as one image of a face. Upon such estimation, we can synthesize the face image under any prescribed new lighting condition. In contrast to commonly used face shape models or shape-dependent models, we neither recover nor assume the 3-D face shape during the estimation process. Instead, we train a pose- and pixel-dependent subspace model of the reflectance function using a face database that contains samples of pose and illumination for a large number of individuals (e.g., the CMU PIE database and the Yale database). Using this subspace model, we can estimate the pose, the reflectance functions, and the lighting condition of any given face image. Our approach lends itself to practical applications thanks to many desirable properties, including the preservation of the non-Lambertian skin reflectance properties and facial hair, as well as reproduction of various shadows on the face. Extensive experiments show that, compared to recent representative face relighting techniques, our method successfully produces better results, in terms of subjective and objective quality, without reconstructing a 3-D shape.
An efficient method for human pointing estimation for robot interaction

In this paper, we propose an efficient calibration method to estimate the pointing direction via a human pointing gesture to facilitate robot interaction. The ways in which pointing gestures are used by humans to indicate an object are individually diverse. In addition, people do not always point at the object carefully, which means there is a divergence between the line from the eye to the tip of the index finger and the line of sight. Hence, we focus on adapting to these individual ways of pointing to improve the accuracy of target object identification by means of an effective calibration process. We model these individual ways as two offsets, the horizontal offset and the vertical offset. After locating the head and fingertip positions, we learn these offsets for each individual through a training process with the person pointing at the camera. Experimental results show that our proposed method outperforms other conventional head-hand, head-fingertip, and eye-fingertip-based pointing recognition methods.
Hierarchical matching for retrieval of hand-drawn sketches

As pen-based devices become more popular, it has motivated several novel research issues in the area of pen computing. One interesting and practical problem is the storage and retrieval of hand-drawn sketches. A sketch can consist of handwritten notes, symbols, free-form hand-drawings, annotations on a document, and others. It is very useful to store the sketches in a database and then retrieve them later. In our prior work, we proposed a method for hand-drawn sketch retrieval by performing stroke-by-stroke matching and by considering the spatial relationship between them. In this paper, we propose a method to simplify a sketch and perform matching in a hierarchical manner. With this approach, a shaded region or a region of complex strokes can be detected automatically and represented as a single hyper-stroke. In the matching stage, the similarity is computed by combining the similarity scores across the feature spaces at each level and the similarity of the stroke hierarchies. A sketch may be better represented by exploiting the structural relationship between the strokes. In addition, as a general rule to the hierarchical approach, it has the advantage of reduced computation thus the retrieval process would speed up.
Nonuniform decimation and reconstruction of generalized-bandlimited MD signals

The nonuniform decimation and reconstruction of generalized-bandlimited multidimensional (MD) signals are discussed. An example which shows the feasibility of reconstructing an MD signal from its nonuniformly decimated version. This idea is extended to the general case, and the theory and design of synthesis filters for reconstruction are discussed. The conditions for the feasibility of perfect reconstruction and the efficient polyphase implementation of synthesis filters are addressed.<<ETX>>