Hypergraph Modelling for Geometric Model Fitting

In this paper, we propose a novel hypergraph based method (called HF) to fit and segment multi-structural data. The proposed HF formulates the geometric model fitting problem as a hypergraph partition problem based on a novel hypergraph model. In the hypergraph model, vertices represent data points and hyperedges denote model hypotheses. The hypergraph, with large and "data-determined" degrees of hyperedges, can express the complex relationships between model hypotheses and data points. In addition, we develop a robust hypergraph partition algorithm to detect sub-hypergraphs for model fitting. HF can effectively and efficiently estimate the number of, and the parameters of, model instances in multi-structural data heavily corrupted with outliers simultaneously. Experimental results show the advantages of the proposed method over previous methods on both synthetic data and real images.Comment: Pattern Recognition, 201

Rank-statistics based enrichment-site prediction algorithm developed for chromatin immunoprecipitation on chip experiments

Background: High density oligonucleotide tiling arrays are an effective and powerful platform for conducting unbiased genome-wide studies. The ab initio probe selection method employed in tiling arrays is unbiased, and thus ensures consistent sampling across coding and non-coding regions of the genome. Tiling arrays are increasingly used in chromatin immunoprecipitation (IP) experiments (ChIP on chip). ChIP on chip facilitates the generation of genome-wide maps of in-vivo interactions between DNA-associated proteins including transcription factors and DNA. Analysis of the hybridization of an immunoprecipitated sample to a tiling array facilitates the identification of ChIP-enriched segments of the genome. These enriched segments are putative targets of antibody assayable regulatory elements. The enrichment response is not ubiquitous across the genome. Typically 5 to 10% of tiled probes manifest some significant enrichment. Depending upon the factor being studied, this response can drop to less than 1%. The detection and assessment of significance for interactions that emanate from non-canonical and/or un-annotated regions of the genome is especially challenging. This is the motivation behind the proposed algorithm. Results: We have proposed a novel rank and replicate statistics-based methodology for identifying and ascribing statistical confidence to regions of ChIP-enrichment. The algorithm is optimized for identification of sites that manifest low levels of enrichment but are true positives, as validated by alternative biochemical experiments. Although the method is described here in the context of ChIP on chip experiments, it can be generalized to any treatment-control experimental design. The results of the algorithm show a high degree of concordance with independent biochemical validation methods. The sensitivity and specificity of the algorithm have been characterized via quantitative PCR and independent computational approaches. Conclusion: The algorithm ranks all enrichment sites based on their intra-replicate ranks and inter-replicate rank consistency. Following the ranking, the method allows segmentation of sites based on a meta p-value, a composite array signal enrichment criterion, or a composite of these two measures. The sensitivities obtained subsequent to the segmentation of data using a meta p-value of 10(-5), an array signal enrichment of 0.2 and a composite of these two values are 88%, 87% and 95%, respectively

Diskriminativni korelacijski filter s segmentacijo in uporabo konteksta za robustno sledenje

Visual object tracking is an area in the field of computer vision, which has seen great popularity increase due to a large availability of video data. There are many different tracking tasks, such as multiple object tracking, long-term tracking and specialized trackers, expected to perform well in a very specific domain. In this work, we focus on online generic short-term single object tracking, which can be considered the base visual tracking task and can be adaptable to any of the previously mentioned tasks. We propose a new tracker, based on correlation filtering, augmented with context information and a predicted object segmentation mask. The results on benchmarks fall far behind the current state-of-the-art, however the proposed method consistently outperforms baseline trackers, which shows the methods potential for future improvements.Vizualno sledenje objektom je področje računalniškega vida, ki je v zadnjih letih doživelo velik razcvet, zahvaljujoč dostopnosti video vsebin. Problem lahko razdelimo na več podnalog, na primer sledenje več objektom, dolgoročno sledenje ali specializirano sledenje za točno določeno domeno. V tem delu se omejimo na splošne kratkoročne sledilnike, ki sledijo enemu objektu. To lahko namreč razumemo kot najbolj osnovno nalogo vizualnega sledenja, ki jo lahko razširimo za delovanje na prej omenjenih problemih. V delu predstavimo nov sledilnik, ki temelji na sledenju s korelacijskimi filtri, razširimo pa ga z uporabo kontekstne informacije in segmentacijske maske. V primerjavi z ostalimi sledilniki predlagana metoda sicer ne dosega rezultatov, primerljivih z najmodernejšimi sledilniki, vendar pa dosledno dosega boljše rezultate od osnovnejših sledilnikov, kar kaže na potencial metode za nadaljnje izboljšave

Recovering 6D Object Pose: A Review and Multi-modal Analysis

A large number of studies analyse object detection and pose estimation at visual level in 2D, discussing the effects of challenges such as occlusion, clutter, texture, etc., on the performances of the methods, which work in the context of RGB modality. Interpreting the depth data, the study in this paper presents thorough multi-modal analyses. It discusses the above-mentioned challenges for full 6D object pose estimation in RGB-D images comparing the performances of several 6D detectors in order to answer the following questions: What is the current position of the computer vision community for maintaining "automation" in robotic manipulation? What next steps should the community take for improving "autonomy" in robotics while handling objects? Our findings include: (i) reasonably accurate results are obtained on textured-objects at varying viewpoints with cluttered backgrounds. (ii) Heavy existence of occlusion and clutter severely affects the detectors, and similar-looking distractors is the biggest challenge in recovering instances' 6D. (iii) Template-based methods and random forest-based learning algorithms underlie object detection and 6D pose estimation. Recent paradigm is to learn deep discriminative feature representations and to adopt CNNs taking RGB images as input. (iv) Depending on the availability of large-scale 6D annotated depth datasets, feature representations can be learnt on these datasets, and then the learnt representations can be customized for the 6D problem