We study the problem of human body configuration anal-ysis, more specifically, human parsing and human pose es-timation. These two tasks, i.e. identifying the semantic re-gions and body joints respectively over the human body image, are intrinsically highly correlated. However, pre-vious works

proposed Random Field Topic model for learning semantic re-gions of crowded scenes from highly fragmented trajectories. This model uses the Markov Random Field prior to capture the spatial and temporal depen-dency between tracklets and uses the source-sink prior to guide the learning of semantic regions

This paper presents a nonparametric region competition algorithm which combines scale-space clustering and re-gion competition to segment the image. It also proposes a formal and general procedure to automatically find the ini-tial regions. Our algorithm can also segment an image into regions which

There is a general trend in recent methods to use image re-gions (i.e. super-pixels) obtained in an unsupervised way to enhance the semantic image segmentation task. This pa-per proposes a detailed study on the role and the benefit of using these regions, at different steps of the segmentation

-scriptions to potentially context-sensitive re-gions in HSV color space. 1

propose to use a Hidden Markov Model (HMM) with a Bag-of-Visual-Words descriptor of image re-gions. An HMM is well-suited for this purpose in that 1) the hidden states, obtained by unsupervised learning, can rep-resent latent semantic concepts, 2) the prior distribution of the hidden states describes

the re-gions to the open source Tesseract1 OCR engine for recognition. We tested our system on a corpus of 8 presentation videos for a total of 1 hour and 45 minutes, achieving 0.5343 Precision and 0.7446 Recall Character recognition rates, and 0.4947 Precision and 0.6651 Recall Word recognition rates

We study the problem of learning to recognise objects in the context of autonomous agents. We cast object recognition as the process of attaching meaningful concepts to specific re-gions of an image. In other words, given a set of images and their captions, the goal is to segment the image

. In this paper, we use an iterative method that keeps refining the segmentation and producing semantic class labels at the same time. The segmentation algorithm is based on a re-gion growing technique and the interpretation is a Markov Random Field (MRF) based classification. The two pro-cesses are integrated

Summary. Ornithomorphic mythological characters, connected with process of cos-mogony and an archetype of Great Mother in mythological concepts of people of the Volga re-gion are considered in this article. In semantic structure of their images the aspects locating concepts of these people about