nils-herrmann/clean_scirepeval huggingface dataset:
Character Recognition in Natural Scenes Using Convolutional Co-occurrence HOG

Recognition of characters in natural images is a challenging task due to the complex background, variations of text size and perspective distortion, etc. Traditional optical character recognition (OCR) engine cannot perform well on those unconstrained text images. A novel technique is proposed in this paper that makes use of convolutional cooccurrence histogram of oriented gradient (ConvCoHOG), which is more robust and discriminative than both the histogram of oriented gradient (HOG) and the co-occurrence histogram of oriented gradients (CoHOG). In the proposed technique, a more informative feature is constructed by exhaustively extracting features from every possible image patches within character images. Experiments on two public datasets including the ICDAr 2003 Robust Reading character dataset and the Street View Text (SVT) dataset, show that our proposed character recognition technique obtains superior performance compared with state-of-the-art techniques.
Neurosphere fate prediction: An analysis-synthesis approach for feature extraction

The study of stem cells is one of the current most important biomedical research field. Understanding their development could allow multiple applications in regenerative medicine. For this purpose, we need automated methods for the segmentation and the modeling of neural stem cell development process into a neurosphere colony from phase contrast microscopy. We use such methods to extract relevant structural and textural features like cell division dynamism and cell behavior patterns for biological interpretation. The combination of phase contrast imaging, high fragility and complex evolution of neural stem cells pose many challenges in image processing and image analysis. This study introduces an on-line analysis method for the modeling of neurosphere evolution during the first three days of their development. From the corresponding time-lapse sequences, we extract information from the neurosphere using a combination of fast level set and curve detection for segmenting the cells. Then, based on prior biological knowledge, we generate possible and optimal 3-dimensional configuration using registration and evolutionary optimisation algorithm.
Harnessing location-context for content-based services in vehicular systems

By incorporating personalised content as context, vehicular telematic services can be made more user-centric, as opposed to vehicle-centric. We develop an ontology based model to integrate both context and content in one unifying framework to facilitate the creation of such services. Scenes captured spontaneously by the user can be a useful index to his or her intent and interests. Our empirical study shows that location-con text leads to a higher precision in recognizing these captured scenes. A "tourist information" prototype based on scene recognition is built, to illustrate one such location-enhanced, user-centric service.
Automated Layer Segmentation of Optical Coherence Tomography Images

Under the framework of computer-aided diagnosis, optical coherence tomography (OCT) has become an established ocular imaging technique that can be used in glaucoma diagnosis by measuring the retinal nerve fiber layer thickness. This letter presents an automated retinal layer segmentation technique for OCT images. In the proposed technique, an OCT image is first cut into multiple vessel and nonvessel sections by the retinal blood vessels that are detected through an iterative polynomial smoothing procedure. The nonvessel sections are then filtered by a bilateral filter and a median filter that suppress the local image noise but keep the global image variation across the retinal layer boundary. Finally, the layer boundaries of the filtered nonvessel sections are detected, which are further classified to different retinal layers to determine the complete retinal layer boundaries. Experiments over OCT for four subjects show that the proposed technique segments an OCT image into five layers accurately.
Efficient Multi-attribute Similarity Learning Towards Attribute-Based Fashion Search

In this paper, we propose an attribute-based query & retrieval system designed for fashion products. Our system addresses the problem of carrying out fashion searches by the query image and attribute manipulation, e.g. replacing long sleeve attribute of a dress to sleeveless. We present the attributes in two groups: (1) general attributes (category, gender etc.) and (2) special attributes (sleeve length, collar etc.). The special attributes are more suitable for the attribute manipulation and thus conducting searches. In order to solve the mentioned fashion search problem, it is crucial for the deep neural networks to understand attribute similarities. To facilitate more specific similarity learning, clothing items are represented by their structural subcomponents or "parts". The parts are estimated using an unsupervised segmentation method and used inside the proposed Convolutional Neural Network (CNN) as an attention mechanism. Meaning, different parts are connected to the special attributes, e.g. sleeve part is connected with sleeve length attribute. With this mechanism, part-based triplet ranking constraint is applied to learn similarity of each special attribute independently from one another in a single network. In the end, the well-defined features are used to conduct the fashion search. Additionally, an adaptive relevance feedback module is used to personalize the fashion search process with the feature descriptions. For our experiments, a new dataset is constructed containing 101,021 images which consist of pure clothing items. Besides achieving decent retrieval results in our dataset, the experiments show that proposed technique outperforms different baselines and is able to adapt towards user's requests.
Computer-aided cataract detection using enhanced texture features on retro-illumination lens images

Cataract is a leading cause of blindness worldwide. Computer-aided cataract detection is two-fold significant. Firstly, it will be helpful in mass screening. Secondly, it can be used as the preprocessing step for computer-aided grading. In this paper, the enhanced texture feature is proposed based on the graders' expertise of cataract and the characteristics of the retro-illumination lens images. The statistics of the enhanced texture feature is used to train the linear discriminant analysis to detect the cataract. The accuracy of 84.8% is achieved on a clinical database that contains 4545 pairs of images. It demonstrates that the proposed method is promising for mass screening and as the preprocessing step for computer-aided grading.
Cascading classifiers for consumer image indexing

We propose a cascading framework of binary classifiers to extract and combine intra-image and inter-class semantics for image indexing and retrieval. Support vector detectors are first trained on semantic support regions without image segmentation. The reconciled and aggregated detection-based indexes then serve as input for support vector learning of image classifiers to generate class-relative image indexes. During retrieval, similarities based on both indexes are combined to rank images. Query by-example experiments on 2400 heterogeneous consumer photos with 16 semantic queries show that the combined matching approach is better than matching with single index. It also outperformed combined matching of color and texture features by 55% in average precision.
Fuzzy Associative Conjuncted Maps Network

The fuzzy associative conjuncted maps (FASCOM) is a fuzzy neural network that associates data of nonlinearly related inputs and outputs. In the network, each input or output dimension is represented by a feature map that is partitioned into fuzzy or crisp sets. These fuzzy sets are then conjuncted to form antecedents and consequences, which are subsequently associated to form if-then rules. The associative memory is encoded through an offline batch mode learning process consisting of three consecutive phases. The initial unsupervised membership function initialization phase takes inspiration from the organization of sensory maps in our brains by allocating membership functions based on uniform information density. Next, supervised Hebbian learning encodes synaptic weights between input and output nodes. Finally, a supervised error reduction phase fine-tunes the network, which allows for the discovery of the varying levels of influence of each input dimension across an output feature space in the encoded memory. In the series of experiments, we show that each phase in the learning process contributes significantly to the final accuracy of prediction. Further experiments using both toy problems and real-world data demonstrate significant superiority in terms of accuracy of nonlinear estimation when benchmarked against other prominent architectures and exhibit the network's suitability to perform analysis and prediction on real-world applications, such as traffic density prediction as shown in this paper.
A Probabilistic Model of Social Working Memory for Information Retrieval in Social Interactions

Social working memory (SWM) plays an important role in navigating social interactions. Inspired by studies in psychology, neuroscience, cognitive science, and machine learning, we propose a probabilistic model of SWM to mimic human social intelligence for personal information retrieval (IR) in social interactions. First, we establish a semantic hierarchy as social long-term memory to encode personal information. Next, we propose a semantic Bayesian network as the SWM, which integrates the cognitive functions of accessibility and self-regulation. One subgraphical model implements the accessibility function to learn the social consensus about IR-based on social information concept, clustering, social context, and similarity between persons. Beyond accessibility, one more layer is added to simulate the function of self-regulation to perform the personal adaptation to the consensus based on human personality. Two learning algorithms are proposed to train the probabilistic SWM model on a raw dataset of high uncertainty and incompleteness. One is an efficient learning algorithm of Newton’s method, and the other is a genetic algorithm. Systematic evaluations show that the proposed SWM model is able to learn human social intelligence effectively and outperforms the baseline Bayesian cognitive model. Toward real-world applications, we implement our model on Google Glass as a wearable assistant for social interaction.
Automatic optic disc detection through background estimation

This paper presents an automatic optic disc (OD) detection technique. Given a retinal image, the proposed method first estimates a retinal background surface through an iterative Savitzky-Golay smoothing procedure. The OD is then detected through the global thresholding of the difference between the retinal image and the estimated background surface. Finally, an OD boundary is determined after a pair of morphological post-processing operations. The proposed technique has been tested over three public datasets that are composed of 130, 89, and 40 retinal images, respectively. Experiments show that an average OD detection accuracy of 96.91% is attained. In addition, 84.37% OD pixels are correctly located compared with the manually labeled ones.
Segmentation of neural stem cells/neurospheres in high content brightfield microscopy images using localized level sets

Neural stem cells and neural progenitors are early nervous system cells that form neurospheres when propagated in vitro. We study changes in growth using brightfield images to understand the effects of drugs. The image quality is generally poor, imposing challenges for automatic analysis. Level-set segmentation methods are able to handle topology changes but require close initializations for accurate and efficient results. Global level-set methods using single image-wide optimization objective functions are difficult to cope with large illumination and shading changes. We propose to adopt Hough transform to initialize localized level-sets for cell segmentation. Experimental results on 480 images with 738 neurospheres show that our proposed method performs best over existing level-set methods without appropriate initial contours.
Whole space subclass discriminant analysis for face recognition

In this work, we propose to divide each class (a person) into subclasses using spatial partition trees which helps in better capturing the intra-personal variances arising from the appearances of the same individual. We perform a comprehensive analysis on within-class and within-subclass eigen-spectrums of face images and propose a novel method of eigen-spectrum modeling which extracts discriminative features of faces from both within-subclass and total or between-subclass scatter matrices. Effective low-dimensional face discriminative features are extracted for face recognition (FR) after performing discriminant evaluation in the entire eigenspace. Experimental results on popular face databases (AR, FERET) and the challenging unconstrained YouTube Face database show the superiority of our proposed approach on all three databases.
Classification of marine organisms in underwater images using CQ-HMAX biologically inspired color approach

In many coastal environments, particularly in tropical zones, coral reef ecosystems have exceptional biodiversity, contribute to coastal defense, provide unique and important habitats and valuable commercial resources. Assessment of environmental impacts on biodiversity in such areas are increasingly important to mitigate potential adverse effects on specific ecosystems. Visual classification of marine organisms is necessary for population estimates of individual species of corals or other benthic organisms. In this paper, we introduce a new image dataset of benthic organisms that are of different colors, shapes, scales, visibility and are taken from different viewpoints. We evaluate several different classification approaches on this dataset, and show that CQ-HMAX, our new biologically inspired approach to utilizing color information for object and scene recognition, that is inspired by the characteristics of color- and object-selective neurons in the high-level inferotemporal (IT) cortex of the primate visual system, results in better classification results in comparison with existing computational models such as support vectors machines, SIFT based approaches and the HMAX biologically inspired approach. We show that concatenating our model which encodes color information with the HMAX model which encodes grayscale shape information results in the highest classification accuracy.
Showroom introduction using mobile phone based on scene image recognition

In this paper, image recognition is used to link a picture to relevant information. As mobile camera phone becoming more and more popular, applications for these phones will bring value to the phone and convenience to the users. We develop a system to perform audio introduction for our technology showroom using images taken by the built-in camera of mobile phone. Visitors to the showroom can take a picture of a poster, a physical prototype or any materials using their mobile phone. An audio introduction in his favorite language will be delivered to the visitor. A few schemes are used to improve the accuracy and system reliability, which include discriminative feature selection based on feature distance; repeating queries if result is not correct; user's verification on delivered content; employment of multiple classifiers. Experimental results show that the system performance is promising.
Clustering and use of spatial and frequency information in a biologically inspired approach to image classification

In this paper, we explore the use of spatial and frequency information of features in the biologically inspired model of HMAX. We discuss and refine previous models which use a similar framework and build specialized features which are better tuned to image structures by using unsupervised methods of clustering and picking the most frequent features using the statistics of the occurrence of the features in different spatial zones. Our classification results on the Caltech 101 dataset show significant improvements of up to 6% compared to previous improvements of the biologically inspired model of HMAX.
Augmented Visual Intelligence

We propose an Augmented Visual Intelligence (AVI) framework to assist human in vision- and memory-related tasks. The AVI framework exploits wearable cameras and ambient computing facilities to empower a user's vision and memory functions by answering four types of queries central to visual activities. In particular, the Extended Visual Memory (EVM) model plays a central role in AVI. Learning of EVM stores view-based visual fragments (VF), which are abstracted into high-level visual schemas (VS), both in the visual long-term memory. During inference, the visual short-term memory plays a key role in the schematic representations of, and the similarity computation between, a visual input and a VF, exemplified from VS when necessary. In this paper, we describe the AVI framework and the EVM model followed by an implementation scenario on assisted living.
Learning associations of conjuncted fuzzy sets for data prediction

Fuzzy associative conjuncted maps (FASCOM) is a fuzzy neural network that represents information by conjuncting fuzzy sets and associates them through a combination of unsupervised and supervised learning. The network first quantizes input and output feature maps using fuzzy sets. They are subsequently conjuncted to form antecedents and consequences, and associated to form fuzzy if-then rules. These associations are learnt through a learning process consisting of three consecutive phases. First, an unsupervised phase initializes based on information density the fuzzy membership functions that partition each feature map. Next, a supervised Hebbian learning phase encodes synaptic weights of the input-output associations. Finally, a supervised error reduction phase fine-tunes the fine-tunes the network and discovers the varying influence of an input dimension across output feature space. FASCOM was benchmarked against other prominent architectures using data taken from three nonlinear data estimation tasks and a real-world road traffic density prediction problem. The promising results compiled show significant improvements over the state-of-the-art for all four data prediction tasks.
INSIDE: a connectionist case-based diagnostic expert system that learns incrementally

The authors describe a connectionist case-based diagnostic expert system that can learn while the system is being used. The system, called INSIDE (Inertial Navigation System Interactive Diagnostic Expert), was developed for Singapore Airlines to assist the technicians in diagnosing the inertial navigation system used by the airplanes. The system learns from past repair cases and adapts its knowledge base to newly solved cases without having to relearn all the old cases.<<ETX>>
Using densely recorded scenes for place recognition

We investigate the task of efficiently modeling a scene to build a robust place recognition system. We propose an approach which involves densely capturing a place with video recordings to greedily cover as many viewpoints of the place as possible. Our contribution is a framework to (1) effectively exploit the temporal continuity intrinsic in the video sequences to reduce the amount of data to process without losing the unique visual information which describes a place, and (2) train discriminative classifiers with the reduced data for place recognition. We show that our method is more efficient and effective than straightforwardly applying scene or object category recognition methods on the video frames.
The use of optical and sonar images in the human and dolphin brain for image classification

In this paper we propose a new biologically inspired model which simulates the visual pathways in the human brain used for classification of matching optical and sonar derived images. Marine mammals, such as dolphins, that live in waters with poor optical clarity and low light levels such as littoral zones, use a combination of optical vision and biosonar to navigate and hunt for prey. Given that dolphins have evolved a synergistic combination of optical visual input and acoustic/sonar input, the primary focus of this paper is on reaching a similar level of synergy for a diver or Autonomous Underwater Vehicle (AUV) platform equipped with a system to extend the range and resolution of vision in poor ambient visibility. We propose a biologically inspired model that combines and processes visual images acquired via optical and acoustic pathways and show that the combined model enhances the accuracy of automatic classification of target objects in underwater images.
Knowledge-Assisted Medical Image Retrieval

In this paper, we present a knowledge-assisted approach to index and retrieve large volume of medical images. Both images and associated texts are indexed using medical concepts from the Unified Medical Language System (UMLS) meta-thesaurus. We propose a structured learning framework for modular acquisition of medical semantics from images with complementary global and local image indexing schemes. Two fusion approaches are also developed to improve text retrieval using the UMLS-based image indexing: a simple post-query fusion and a visual modality filtering to remove visually aberrant images according to the query modality concepts. On the ImageCLEFmed 2005 database, our framework outperformed our previous result which ranked top in the ImageCLEFmed 2005 Medical Image Retrieval task benchmark.
A Recursive and Model-Constrained Region Splitting Algorithm for Cell Clump Decomposition

Decomposition of cells in clumps is a difficult segmentation task requiring region splitting techniques. Techniques that do not employ prior shape constraints usually fail to achieve accurate segmentation. Those using shape constraints are unable to cope with large clumps and occlusions. In this work, we propose a model-constrained region splitting algorithm for cell clump decomposition. We build the cell model using joint probability distribution of invariant shape features. The shape model, the contour smoothness and the gradient information along the cut are used to optimize the splitting in a recursive manner. The short cut rule is also adopted as a strategy to speed up the process. The algorithm performs well in validation experiments using 60 images with 4516 cells and 520 clumps.
Semantic indexing and retrieval of home photos

With rapid advances in sensor, storage, processor, and communication technologies, consumers can now afford to create, store, process, and share large digital photo collections. With more and more digital photos accumulated, consumers need effective and efficient tools to organize and access photos in a semantically meaningful way without too much manual annotation effort. From user studies, we confirm that users prefer to organize and access photos along semantic axes such as event, people, time, and place. As a matter of fact, research on content-based image retrieval in the last decade is yet to bridge the semantic gap between feature-based indexes computed automatically and human preferences on query and retrieval. In this paper, we attempt to address this semantic gap by focusing on the notion of "event" in home photos. First we propose event taxonomy for home photos. Next we propose a computational learning framework to construct event models from sample photos with event labels given by a user and to compute relevance measures of unlabeled photos to the event models, Last but not least, we demonstrate event-based retrieval on 2400 genuine home photos using our proposed approach.