nils-herrmann/clean_scirepeval huggingface dataset:
Face Detection Using Quantized Skin Color Regions Merging and Wavelet Packet Analysis

Detecting and recognizing human faces automatically in digital images strongly enhance content-based video indexing systems. In this paper, a novel scheme for human faces detection in color images under nonconstrained scene conditions, such as the presence of a complex background and uncontrolled illumination, is presented. Color clustering and filtering using approximations of the YCbCr and HSV skin color subspaces are applied on the original image, providing quantized skin color regions. A merging stage is then iteratively performed on the set of homogeneous skin color regions in the color quantized image, in order to provide a set of potential face areas. Constraints related to shape and size of faces are applied, and face intensity texture is analyzed by performing a wavelet packet decomposition on each face area candidate in order to detect human faces. The wavelet coefficients of the band filtered images characterize the face texture and a set of simple statistical deviations is extracted in order to form compact and meaningful feature vectors. Then, an efficient and reliable probabilistic metric derived from the Bhattacharrya distance is used in order to classify the extracted feature vectors into face or nonface areas, using some prototype face area vectors, acquired in a previous training stage.
Training convolutional filters for robust face detection

We present a face detection approach based on a convolutional neural architecture, designed to detect and precisely localize highly variable face patterns, in complex real world images. Our system automatically synthesizes simple problem-specific feature extractors from a training set of face and non face patterns, without making any assumptions or using any hand-made design concerning the features to extract or the areas of the face pattern to analyze. Experiments on different difficult test sets have shown that our approach provide superior overall detection results, while being computationally more efficient than most of state-of-the-art approaches that require dense scanning and local preprocessing.
Robust Binarization for Video Text Recognition

This paper presents an automatic binarization method for color text areas in images or videos, which is robust to complex background, low resolution or video coding artefacts. Based on a specific architecture of convolutional neural networks, the proposed system automatically learns how to perform binarization, from a training set of synthesized text images and their corresponding desired binary images, without making any assumptions or using tunable parameters. The proposed method is compared to state-of-the-art binarization techniques, with respect to Gaussian noise and contrast variations, demonstrating the robustness and the efficiency of our method. Text recognition experiments on a database of images extracted from video frames and web pages, with two classical OCRs applied on the obtained binary images show a strong enhancement of the recognition rate by more than 40%.
Fast Pixelwise Adaptive Visual Tracking of Non-Rigid Objects

In this paper, we present a new algorithm for real-time single-object tracking in videos in unconstrained environments. The algorithm comprises two different components that are trained “in one shot” at the first video frame: a detector that makes use of the generalized Hough transform with color and gradient descriptors and a probabilistic segmentation method based on global models for foreground and background color distributions. Both components work at pixel level and are used for tracking in a combined way adapting each other in a co-training manner. Moreover, we propose an adaptive shape model as well as a new probabilistic method for updating the scale of the tracker. Through effective model adaptation and segmentation, the algorithm is able to track objects that undergo rigid and non-rigid deformations and considerable shape and appearance variations. The proposed tracking method has been thoroughly evaluated on challenging benchmarks, and outperforms the state-of-the-art tracking methods designed for the same task. Finally, a very efficient implementation of the proposed models allows for extremely fast tracking.
Using Discriminative Motion Context for Online Visual Object Tracking

In this paper, we propose an algorithm for online, real-time tracking of arbitrary objects in videos from unconstrained environments. The method is based on a particle filter framework using different visual features and motion prediction models. We effectively integrate a discriminative online learning classifier into the model and propose a new method to collect negative training examples for updating the classifier at each video frame. Instead of taking negative examples only from the surroundings of the object region, or from specific background regions, our algorithm samples the negatives from a contextual motion density function in order to learn to discriminate the target as early as possible from potential distracting image regions. We experimentally show that this learning scheme improves the overall performance of the tracking algorithm. Moreover, we present quantitative and qualitative results on four challenging public data sets that show the robustness of the tracking algorithm with respect to appearance and view changes, lighting variations, partial occlusions, as well as object deformations. Finally, we compare the results with more than 30 state-of-the-art methods using two public benchmarks, showing very competitive results.
3D gesture classification with convolutional neural networks

In this paper, we present an approach that classifies 3D gestures using jointly accelerometer and gyroscope signals from a mobile device. The proposed method is based on a convolutional neural network with a specific structure involving a combination of 1D convolution, averaging, and max-pooling operations. It directly classifies the fixed-length input matrix, composed of the normalised sensor data, as one of the gestures to be recognises. Experimental results on different datasets with varying training/testing configurations show that our method outperforms or is on par with current state-of-the-art methods for almost all data configurations.
Sparse Coding with a Coupled Dictionary Learning Approach for Textual Image Super-resolution

Sparse coding is widely known as a methodology where an input signal can be sparsely represented from a suitable dictionary. It was successfully applied on a wide range of applications like the textual image Super-Resolution. Nevertheless, its complexity limits enormously its application. Looking for a reduced computational complexity, a coupled dictionary learning approach is proposed to generate dual dictionaries representing coupled feature spaces. Under this approach, we optimize the training of a first dictionary for the high-resolution image space and then a second dictionary is simply deduced from the latter for the low-resolution image space. In contrast with the classical dictionary learning approaches, the proposed approach allows a noticeable speedup and a major simplification of the coupled dictionary learning phase both in terms of algorithm architecture and computational complexity. Furthermore, the resolution enhancement results achieved by applying the proposed approach on poorly resolved textual images lead to image quality improvements.
Normalized radial basis function networks and bilinear discriminant analysis for face recognition

In this paper, we present a novel approach for face recognition, using a new subspace method called bilinear discriminant analysis (BDA) and normalized radial basis function networks (NRBFNs). In a first step, BDA extracts the features that enhance separation between classes by using a generalized bilinear projection-based Fisher criterion, computed from image matrices directly. In a second step, the features are fed into a NRBFN that learns class conditional probabilities. This results in an efficient and computationally simple open-world identification process. Experimental results assess the performance and robustness of the proposed algorithm compared to other subspace methods combined with NRBFNs, in the presence of variations in head poses, facial expressions, and partial occlusions.
Convolutional face finder: a neural architecture for fast and robust face detection

In this paper, we present a novel face detection approach based on a convolutional neural architecture, designed to robustly detect highly variable face patterns, rotated up to /spl plusmn/20 degrees in image plane and turned up to /spl plusmn/60 degrees, in complex real world images. The proposed system automatically synthesizes simple problem-specific feature extractors from a training set of face and nonface patterns, without making any assumptions or using any hand-made design concerning the features to extract or the areas of the face pattern to analyze. The face detection procedure acts like a pipeline of simple convolution and subsampling modules that treat the raw input image as a whole. We therefore show that an efficient face detection system does not require any costly local preprocessing before classification of image areas. The proposed scheme provides very high detection rate with a particularly low level of false positives, demonstrated on difficult test sets, without requiring the use of multiple networks for handling difficult cases. We present extensive experimental results illustrating the efficiency of the proposed approach on difficult test sets and including an in-depth sensitivity analysis with respect to the degrees of variability of the face patterns.
A connexionist approach for robust and precise facial feature detection in complex scenes

We present a technique for robustly and automatically detect a set of user-selected facial features in images, like the eye pupils, the tip of the nose, the mouth centre, etc. Based on a specific architecture of heterogeneous neural layers, the proposed system automatically synthesises simple problem-specific feature extractors and classifiers from a training set of faces with annotated facial features. After training, the facial feature detection system acts like a pipeline of simple filters that treats the raw input face image as a whole and builds global facial feature maps, where facial feature positions can easily be retrieved by a simple search for global maxima. We experimentally show that our method is very robust to lighting and pose variations as well as noise and partial occlusions.
Fusion based resolution enhancement in hyperspectral images

Increasing the low spatial resolution of hyperspectral images (HSIs) improves the performance of applications in which the HSIs are used. In this study, a fusion based method is proposed to increase the resolution of HSIs. In the proposed method, low resolution (LR) HSI is fused with the high resolution (HR) RGB image to obtain the HR HSI. In this approach, instead of using the spectral images as in the conventional methods, RGB image is used with the abundance maps of the HSI estimated from the linear unmixing and the spatial resolution is enhanced using these maps. In this method, firstly, endmembers are estimated and LR abundance maps are obtained. Then, HR abundance maps are obtained by minimizing an energy function, which is constructed from the LR abundance maps with the HR RGB image. Finally, HR HSI is obtained from these HR abundance maps. The method is tested with real HSIs. Main contribution of the method is converting fusion problem to a quadratic optimization problem in the abundance map domain without any assumption or prior knowledge. The proposed method solves the fusion problem with a computational time much lower than the state-of-the-art fusion based methods with a competing performance.
A MAP-Based Approach for Hyperspectral Imagery Super-Resolution

In this paper, we propose a novel single image Bayesian super-resolution (SR) algorithm where the hyperspectral image (HSI) is the only source of information. The main contribution of the proposed approach is to convert the ill-posed SR reconstruction problem in the spectral domain to a quadratic optimization problem in the abundance map domain. In order to do so, Markov random field based energy minimization approach is proposed and proved that the solution is quadratic. The proposed approach consists of five main steps. First, the number of endmembers in the scene is determined using virtual dimensionality. Second, the endmembers and their low resolution abundance maps are computed using simplex identification via the splitted augmented Lagrangian and fully constrained least squares algorithms. Third, high resolution (HR) abundance maps are obtained using our proposed maximum a posteriori based energy function. This energy function is minimized subject to smoothness, unity, and boundary constraints. Fourth, the HR abundance maps are further enhanced with texture preserving methods. Finally, HR HSI is reconstructed using the extracted endmembers and the enhanced abundance maps. The proposed method is tested on three real HSI data sets; namely the Cave, Harvard, and Hyperspectral Remote Sensing Scenes and compared with state-of-the-art alternative methods using peak signal to noise ratio, structural similarity, spectral angle mapper, and relative dimensionless global error in synthesis metrics. It is shown that the proposed method outperforms the state of the art methods in terms of quality while preserving the spectral consistency.
Fast Mode Decision for Intra Prediction in H.264

In H.264 video coding standard, in order to increase the coding efficiency, several new techniques such as spatial intra prediction, integer transformation have been introduced. However these also increase the computational complexity drastically. In this paper, we propose a new fast intra prediction algorithm to be used instead of the full search algorithm to choose the best mode for spatial intra prediction. Experimental results show that, our algorithm achieves 52% computation reduction on the average while maintaining similar PSNR and an average bitrate increase of 1.5%
Moving vehicle classification

In recent years intelligent transportation systems has been an active research area in computer vision. This work aims to classify vehicles as car, van or truck by using videos which are taken from an uncalibrated camera which sees any highway from above. In this work, moving blobs are detected by a background-foreground extraction algorithm, classification is done by extracting the blob features and histogram of oriented gradients features of detected blobs and by giving those features to support vector machine classifier. Suggested method has been tried on 2 different traffic video and results have been presented.
Visual and Textual Feature Fusion for Automatic Customs Tariff Classification

The Harmonized Tariff Schedule for the classification of goods is a major determinant of customs duties and taxes. The basic HS Code is 6 digits long but can be extended according to the needs of the countries such as application of custom duties based on details of the product. Finding the correct, consistent, legally defensible HS Code is at the heart of Import Compliance. However finding the best code can be complicated, especially in the case of specialized products. In this paper, we propose an automatic HS code detection system based on visual properties of the product together with textual analysis of its labels/explanations. The proposed system first uses morphological parsing in order to extract roots of the words occurring in the textual phrases. Processed text information is further processed by the topic modeling module of the system to find the best matching HS Code definitions within the system. The result of the topic modeling is used to trigger visual search based on quantized local features. The proposed algorithm is evaluated using a database of 4494 Binding Tariffs published in 2014 by the European Union. The results show that accuracy rate above 80 % can be achieved for 4-digit HS Codes.
A circle detection approach based on Radon Transform

In this paper a novel fast circle detection algorithm is proposed which depends on the spatial properties of the connected components on the image. Two 1-D transforms of each connected component is obtained by taking the Radon Transform of the image for two different directions, which are in fact the integrations of the image through horizontal and vertical directions. Circles are detected using the similarities of detected peaks on the transformed functions and the characteristics of the values in between those peaks. The success of the method is analyzed using synthetic images and the performance of the method is presented and compared with Modified Hough Transform (MHT) using synthetic images.
Noise reduction on hyperspectral imagery using spectral unmixing and class-labels

Noise reduction on hyperspectral imagery is a critical step for the success of other applications that use this type of data. In this paper, we propose a novel approach to reduce the noise on hyperspectral data that might occur due to various factors. Since the proposed method exploits class-labels of data, it can be categorized as a semi-supervised method. First, our approach computes the mean spectral signatures of data using their spatial coherence and class-labels, then robust pure material signatures are estimated with different spectral unmixing methods. Later, these signatures are analyzed for the noise reduction. Tests are conducted on Indian Pines dataset under different noise characteristics. The experimental results show that our proposed method improves PSNR scores compared to baseline methods that use either spectral unmixing or class-labels. Furthermore, noticeable improvements on computation complexity are observed.
Optimization of encoding and error protection parameters for 3D Video Broadcast over DVB-H

In this study, we propose a heuristic methodology for modeling the end-to-end distortion characteristics of an error resilient broadcast system for 3D video over Digital Video Broadcasting - Handheld (DVB-H).We also use this model to optimally select the parameters of the video encoder and the error correction scheme, namely, Multi Protocol Encapsulation - Forward Error Correction (MPE-FEC), minimizing the overall distortion. The proposed method models the RQ curve of video encoder and performance of channel codec to jointly derive the optimal encoder bit rates and unequal error protection (UEP) rates specific to the 3D video broadcast. Moreover, the distortion on the 3D video quality caused by packet losses and the loss rate of the channel is estimated. Finally, with the use of analytical models and estimated single packet loss distortions, end-to-end distortions are minimized and optimal encoder bit rates and UEP rates are obtained.
Content aware audiovisual quality assessment

In this study, a novel, content aware audiovisual quality assessment (AVQA) method using a spatio-temporal characteristics based video classification method has been proposed and evaluated on AVQA database created by University of Plymouth. The proposed AVQA method is evaluated using subjective audio mean opinion score (MOS) and subjective video MOS. Results indicate that both classification method and the proposed content dependent AVQA method are quite satisfactory.
Digital Image Watermarking Using Holograms

In image watermarking, holography usage is a very novel approach. Holography, in fact, is a method to store three-dimensional (3-D) information of an object, however, many of its properties make it very applicable in use of watermarking. In this work, a new blind image watermarking approach is proposed, which embeds a hologram as the watermark into a host image. Moreover, the robustness of the method is examined against some attacks like, JPEG compression, noise addition and cropping
Rate-distortion optimized layered stereoscopic video streaming with raptor codes

A near optimal streaming system for stereoscopic video is proposed. Initially, the stereoscopic video is separated into three layers and the approximate analytical model of the Rate-Distortion (RD) curve of each layer is calculated from sufficient number of rate and distortion samples. The analytical modeling includes the interdependency of the defined layers. Then, the analytical models are used to derive the optimal source encoding rates for a given channel bandwidth. The distortion in the quality of the stereoscopic video that is caused by losing a NAL unit from the defined layers is estimated to minimize the average distortion of a single NAL unit loss. The minimization is performed over protection rates allocated to each layer. Raptor codes are utilized as the error protection scheme due to their novelty and suitability in video transmission. The layers are protected unequally using Raptor codes according to the parity ratios allocated to the layers. Comparison of the defined scheme with two other protection allocation schemes is provided via simulations to observe the quality of stereoscopic video.
Super-resolution Reconstruction of hyperspectral images via an improved MAP-based approach

Super-resolution Reconstruction (SRR) is technique to increase the spatial resolution of images. It is especially useful for hyperspectral images (HSI), which have good spectral resolution but low spatial resolution. In this study, we propose an improvement to our previous work and present a novel MAP-MRF (maximum a posteriori-Markov random Fields) based approach for the SRR of HSI. The key point of our approach is to find the abundance maps of an HSI and perform SRR on the abundance maps using MRF based energy minimization, without needing any other additional source of information. In order to do so, first, PCA is used to determine the endmembers. Second, SISAL and fully constraint least squares (FCLS) are used to estimate the abundance maps. Third, in order to find the high resolution abundance maps, the ill-posed inverse SRR problem for abundances is regularized with a MAP-MRF based approach. The MAP-MRF formulation is restricted with the constraints which are specific to the abundances. Using the non-linear programming (NLP) techniques, the convex MAP formulation is minimized and High Resolution (HR) abundance maps are obtained. Then, these maps are used to construct the HR HSI. This improved SRR method is verified on real data sets, and quantitative performance comparison is achieved using PSNR, SSIM and PSNR metrics. Our results indicate that this improved method gives very close results to the original high resolution images, keeps the spectral consistency, and performs better than the compared algorithms.
User directed view synthesis on OMAP processors

In this paper, we propose a system for user directed real time view synthesis for hand-held devices. Stored image frames with corresponding depth maps are used as input to the system. Users view point choice is captured using a GYRO based system. OMAP3530 microprocessor is used as the main processor which processes suggested view synthesis algorithm with occlusion handling and frame enhancement techniques. Proposed algorithms are implemented on DSP core and ARM core of OMAP3530 separately and their performances are evaluated through experiments. In addition, two daughter cards are designed for user view point determination. First daughter card handles communication process with EVM board and calculates view point according to the input from the second daughter card with single axis response GYRO sensor (ADIS16060).