A Deep-structured Conditional Random Field Model for Object Silhouette Tracking

In this work, we introduce a deep-structured conditional random field (DS-CRF) model for the purpose of state-based object silhouette tracking. The proposed DS-CRF model consists of a series of state layers, where each state layer spatially characterizes the object silhouette at a particular point in time. The interactions between adjacent state layers are established by inter-layer connectivity dynamically determined based on inter-frame optical flow. By incorporate both spatial and temporal context in a dynamic fashion within such a deep-structured probabilistic graphical model, the proposed DS-CRF model allows us to develop a framework that can accurately and efficiently track object silhouettes that can change greatly over time, as well as under different situations such as occlusion and multiple targets within the scene. Experiment results using video surveillance datasets containing different scenarios such as occlusion and multiple targets showed that the proposed DS-CRF approach provides strong object silhouette tracking performance when compared to baseline methods such as mean-shift tracking, as well as state-of-the-art methods such as context tracking and boosted particle filtering.Comment: 17 page

Deep-MDS Framework for Recovering the 3D Shape of 2D Landmarks from a Single Image

In this paper, a low parameter deep learning framework utilizing the Non-metric Multi-Dimensional scaling (NMDS) method, is proposed to recover the 3D shape of 2D landmarks on a human face, in a single input image. Hence, NMDS approach is used for the first time to establish a mapping from a 2D landmark space to the corresponding 3D shape space. A deep neural network learns the pairwise dissimilarity among 2D landmarks, used by NMDS approach, whose objective is to learn the pairwise 3D Euclidean distance of the corresponding 2D landmarks on the input image. This scheme results in a symmetric dissimilarity matrix, with the rank larger than 2, leading the NMDS approach toward appropriately recovering the 3D shape of corresponding 2D landmarks. In the case of posed images and complex image formation processes like perspective projection which causes occlusion in the input image, we consider an autoencoder component in the proposed framework, as an occlusion removal part, which turns different input views of the human face into a profile view. The results of a performance evaluation using different synthetic and real-world human face datasets, including Besel Face Model (BFM), CelebA, CoMA - FLAME, and CASIA-3D, indicates the comparable performance of the proposed framework, despite its small number of training parameters, with the related state-of-the-art and powerful 3D reconstruction methods from the literature, in terms of efficiency and accuracy

Single sample face identification utilizing sparse discriminative multi manifold embedding

This paper describes three methods to improve single sample dataset face identification. The recent approaches to address this issue use intensity and do not guarantee for the high accuracy under uncontrolled conditions. This research presents an approach based on Sparse Discriminative Multi Manifold Embedding (SDMME) , which uses feature extraction rather than intensity and normalization for pre–processing to reduce the effects of uncontrolled condition such as illumination. In average this study improves identification accuracy about 17% compare to current method

Multi-Projector Content Preservation with Linear Filters

Using aligned overlapping image projectors provides several ad-vantages when compared to a single projector: increased bright-ness, additional redundancy, and increased pixel density withina region of the screen. Aligning content between projectors isachieved by applying space transformation operations to the de-sired output. The transformation operations often degrade the qual-ity of the original image due to sampling and quantization. Thetransformation applied for a given projector is typically done in iso-lation of all other content-projector transformations. However, it ispossible to warp the images with prior knowledge of each othersuch that they utilize the increase in effective pixel density. Thisallows for an increase in the perceptual quality of the resultingstacked content. This paper presents a novel method of increas-ing the perceptual quality within multi-projector configurations. Amachine learning approach is used to train a linear filtering basedmodel that conditions the individual projected images on each othe

Fast Radiometric Compensation for Nonlinear Projectors

Radiometric compensation can be accomplished on nonlinearprojector-camera systems through the use of pixelwise lookup ta-bles. Existing methods are both computationally and memory inten-sive. Such methods are impractical to be implemented for currenthigh-end projector technology. In this paper, a novel computation-ally efficient method for nonlinear radiometric compensation of pro-jectors is proposed. The compensation accuracy of the proposedmethod is assessed with the use of a spectroradiometer. Experi-mental results show both the effectiveness of the method and thereduction in compensation time compared to a recent state-of-the-art method

Text Enhancement in Projected Imagery

There is great interest in improving the visual quality of projectedimagery. In particular, for image enhancement, we would assertthat text and non-text regions should be enhanced differently inseeking to maximize perceived quality, since the spatial and statis-tical characteristics of text and non-text images are quite distinct.In this paper, we present a text enhancement scheme based on anovel local dynamic range statistical thresholding. Given an inputimage, text-like regions are obtained on the basis of computing thelocal statistics of regions having a high dynamic range, allowing apixel-wise classification into text-like or background classes. Theactual enhancement is obtained via class-dependent Wiener filter-ing, with text-like regions sharpened more than the background.Experimental results on four challenging images show that the pro- posed scheme offers a better visual quality than projection with- out enhancement as well as a recent state-of-the-art enhancementmethod