Evaluation of Local Feature Detectors for the Comparison of Thermal and Visual Low Altitude Aerial Images

Local features are key regions of an image suitable for applications such as image matching, and fusion. Detection of targets under varying atmospheric conditions, via aerial images is a typical defence application where multi spectral correlation is essential. Focuses on local features for the comparison of thermal and visual aerial images in this study. The state of the art differential and intensity comparison based features are evaluated over the dataset. An improved affine invariant feature is proposed with a new saliency measure. The performances of the existing and the proposed features are measured with a ground truth transformation estimated for each of the image pairs. Among the state of the art local features, Speeded Up Robust Feature exhibited the highest average repeatability of 57 per cent. The proposed detector produces features with average repeatability of 64 per cent. Future works include design of techniques for retrieval of corresponding regions

A Comparison Study of Saliency Models for Fixation Prediction on Infants and Adults

Various saliency models have been developed over the years. The performance of saliency models is typically evaluated based on databases of experimentally recorded adult eye fixations. Although studies on infant gaze patterns have attracted much attention recently, saliency based models have not been widely applied for prediction of infant gaze patterns. In this study, we conduct a comprehensive comparison study of eight state-ofthe- art saliency models on predictions of experimentally captured fixations from infants and adults. Seven evaluation metrics are used to evaluate and compare the performance of saliency models. The results demonstrate a consistent performance of saliency models predicting adult fixations over infant fixations in terms of overlap, center fitting, intersection, information loss of approximation, and spatial distance between the distributions of saliency map and fixation map. In saliency and baselines models performance ranking, the results show that GBVS and Itti models are among the top three contenders, infants and adults have bias toward the centers of images, and all models and the center baseline model outperformed the chance baseline model

Saliency-based identification and recognition of pointed-at objects

Abstract — When persons interact, non-verbal cues are used to direct the attention of persons towards objects of interest. Achieving joint attention this way is an important aspect of natural communication. Most importantly, it allows to couple verbal descriptions with the visual appearance of objects, if the referred-to object is non-verbally indicated. In this contri-bution, we present a system that utilizes bottom-up saliency and pointing gestures to efficiently identify pointed-at objects. Furthermore, the system focuses the visual attention by steering a pan-tilt-zoom camera towards the object of interest and thus provides a suitable model-view for SIFT-based recognition and learning. We demonstrate the practical applicability of the proposed system through experimental evaluation in different environments with multiple pointers and objects

Large Scale Pattern Detection in Videos and Images from the Wild

PhDPattern detection is a well-studied area of computer vision, but still current methods are unstable in images of poor quality. This thesis describes improvements over contemporary methods in the fast detection of unseen patterns in a large corpus of videos that vary tremendously in colour and texture definition, captured “in the wild” by mobile devices and surveillance cameras. We focus on three key areas of this broad subject; First, we identify consistency weaknesses in existing techniques of processing an image and it’s horizontally reflected (mirror) image. This is important in police investigations where subjects change their appearance to try to avoid recognition, and we propose that invariance to horizontal reflection should be more widely considered in image description and recognition tasks too. We observe online Deep Learning system behaviours in this respect, and provide a comprehensive assessment of 10 popular low level feature detectors. Second, we develop simple and fast algorithms that combine to provide memory- and processing-efficient feature matching. These involve static scene elimination in the presence of noise and on-screen time indicators, a blur-sensitive feature detection that finds a greater number of corresponding features in images of varying sharpness, and a combinatorial texture and colour feature matching algorithm that matches features when either attribute may be poorly defined. A comprehensive evaluation is given, showing some improvements over existing feature correspondence methods. Finally, we study random decision forests for pattern detection. A new method of indexing patterns in video sequences is devised and evaluated. We automatically label positive and negative image training data, reducing a task of unsupervised learning to one of supervised learning, and devise a node split function that is invariant to mirror reflection and rotation through 90 degree angles. A high dimensional vote accumulator encodes the hypothesis support, yielding implicit back-projection for pattern detection.European Union’s Seventh Framework Programme, specific topic “framework and tools for (semi-) automated exploitation of massive amounts of digital data for forensic purposes”, under grant agreement number 607480 (LASIE IP project)

OMap: An assistive solution for identifying and localizing objects in a semi-structured environment

A system capable of detection and localization of objects of interest in a semi-structured environment will enhance the quality of life of people who are blind or visually impaired. Towards building such a system, this thesis presents a personalized real-time system called O\u27Map that finds misplaced/moved personal items and localizes them with respect to known landmarks. First, we adopted a participatory design approach to identify users’ need and functionalities of the system. Second, we used the concept from system thinking and design thinking to develop a real-time object recognition engine that was optimized to run on low form factor devices. The object recognition engine finds robust correspondences between the query image and item templates using K-D tree of invariant feature descriptor with two nearest neighbors and ratio test. Quantitative evaluation demonstrates that O\u27Map identifies object of interest with an average F-measure of 0.9650

Image recognition for robotic hand

Tato práce se zabývá zpracováním snímků displeje embedded zařízení a jejich klasifikací. Je zde rozebrána problematika odstranění šumu moaré prostřednictvím filtrace ve spektru a normalizace obrazu pro další analýzu. Pro klasifikaci obrazů jsou využity detektory významných bodů a deskriptory. Hlavní důraz je kladen na detektory FAST a Harrisův detektor rohů a na deskriptory SURF, BRIEF a BRISK a jejich hodnocení z pohledu potenciálního přínosu pro tuto práci.This thesis concerns with processing of embedded terminals’ images and their classification. There is problematics of moire noise reduction thought filtration in frequency domain and the image normalization for further processing analyzed. Keypoints detectors and descriptors are used for image classification. Detectors FAST and Harris corner detector and descriptors SURF, BRIEF and BRISK are emphasized as well as their evaluation in terms of potential contribution to this work.