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This paper proposes efficient and powerful deep networks for action prediction from partially observed videos containing temporally incomplete action executions. Different from after-the-fact action recognition, action prediction task requires action labels to be predicted from these partially observed videos. Our approach exploits abundant sequential context information to enrich the feature representations...
When considering person re-identification (re-ID) as a retrieval process, re-ranking is a critical step to improve its accuracy. Yet in the re-ID community, limited effort has been devoted to re-ranking, especially those fully automatic, unsupervised solutions. In this paper, we propose a k-reciprocal encoding method to re-rank the re-ID results. Our hypothesis is that if a gallery image is similar...
Speech is the natural and vocalized form of human communication. Automatic Speech Recognition makes the computer to understand what was spoken by the speaker. Power Normalized Cepstral Coefficients (PNCC) is a feature extraction technique that uses power law non linearity. When the environmental condition changes, the accuracy of the system degrades. PNCC provides better enhancement in acoustical...
Fractal analysis has been widely used in computer vision, especially in texture image processing and texture analysis. The key concept of fractal-based image model is the fractal dimension, which is invariant to bi-Lipschitz transformation of image, and thus capable of representing intrinsic structural information of image robustly. However, the invariance of fractal dimension generally does not hold...
Geospatial object detection from high spatial resolution (HSR) imagery is significant and challenging for further analyzing the object-related information in various civil and military applications. Traditional object detection methods based on the handcrafted features are limited by their efficiency in describing the multi-class objects from large-swath and complex-context HSR imagery. Although convolutional...
Geographical Information System (GIS) is used to collect, manipulate, analyze, and display the geospatial data. The compilation and management of this spatial data is expensive and time consuming task. Due to rapid growth of distributed networks and Internet, it becomes easy to handle data but at the same time it becomes easy to copy or distribute the spatial data. Therefore copyright protection,...
In this paper, an invisible watermarking algorithm for satellite imagery using Curvelet Transform is proposed. The Raster image is split into smaller non-overlapping blocks. Haralick Co-occurrence texture features [1] are used to identify the area for embedding watermark in these blocks of Raster image. Thus multiple watermarks are embedded in any given image. Edges are selected for embedding watermark...
Facial change with age is always a problem in facial features matching for photos of ID cards. In order to solve this problem, a novel facial features matching method based on SIFT algorithm is proposed. Inspired by attention mechanism of the visual system, the facial features focuses on the four sub regions (left eye, right eye, nose and mouth), ignoring other unimportant regions, and different weights...
This paper addresses the problem of exploiting very high-resolution multifrequency SAR data collected by the COSMO-SkyMed and RADARSAT-2 missions to support risk monitoring and assessment in urban and suburban areas. The proposed approach aims at taking benefit from the synergy between the two SAR data sources to optimize the accuracy of thematic products of interest to risk monitoring. In particular,...
We investigate and validate feature-based registration techniques for remotely sensed satellite images. Feature-based registration algorithms seek to detect image features such as boundaries, corners, segment intersections which are used for matching. We implemented some of the state-of-the-art feature detection, extraction and matching techniques, which are BRISK, FAST, HARRIS, Minimum eigenvalues,...
Change detection in multitemporal hyperspectral images (HSI) can be regarded as a classification task, consisting of two steps: change feature extraction and identification. To extract clean change features from heavily corrupted spectral change vectors (SCV) of multitemporal HSI, this paper proposes a novel spectrally-spatially regularized low-rank and sparse decomposition model (LRSDSS). It exploits...
In this paper, a new heterogeneous neural networks based deep learning method, named HNNDL, is presented for supervised classification of hyperspectral image (HSI) with a small number of labeled samples. Specifically, a deep neural Network (DNN) and a convolutional neural network (CNN) are combined to build a HNNDL architecture. The proposed architecture contains three modules: 1) dimension reduction...
Convolutional neural networks (CNNs) has been introduced into remote sensing scene classification, achieving outstanding performance. However, the scale change of objects contained in remote sensing scene image make it difficult to extract feature robust to scale, limiting the further improvement of classification accuracy. In this paper, a scene classification method named Scale Invariance Convolutional...
Detection, and localization of Bangla text from natural scene images are important prerequisites for developing Bangla OCR as well as many content-based image analyses. But there is no standard Bangla OCR to be used in the daily work. Due to the presence of some unique features, detection and localization of Bangla text have become more challenging than English text. In this paper, we have proposed...
WiFi indoor localization has attracted much attention owing to the pervasive penetration of wireless local area networks (WLANs) and WiFi enabled mobile devices. Traditional WiFi indoor localization systems rely on received signal strength (RSS), which is instability and low space distinguish ability. Recently, channel state information (CSI) has been adopted instead of RSS and proven to be an efficient...
Currently, the image registration algorithm is mainly implemented on PC. In this paper, to realize image registration on a mobile terminal, a customized image registration system was designed and implemented on Raspberry Pi microcomputer board, in which the Keren algorithm is adopted to achieve image registration. Experimental results show that this system can reach a sub-pixel level of registration...
Human action classification, which is vital for content-based video retrieval and human-machine interaction, finds problem in distinguishing similar actions. Previous works typically detect spatial-temporal interest points (STIPs) from action sequences and then adopt bag-of-visual words (BoVW) model to describe actions as numerical statistics of STIPs. Despite the robustness of BoVW, this model ignores...
In this paper, we propose a topology preserving graph matching (TPGM) method for partial face recognition. Most existing face recognition methods extract features from holistic face images, yet faces in real-world unconstrained environments are usually occluded by objects or other faces, which cannot provide the whole face images for recognition. Latest keypoint-based partial face recognition methods...
This paper targets to bring together the research efforts on two fields that are growing actively in the past few years: multicamera person Re-Identification (ReID) and large-scale image retrieval. We demonstrate that the essentials of image retrieval and person ReID are the same, i.e., measuring the similarity between images. However, person ReID requires more discriminative and robust features to...
Convolutional neural network (CNN) has drawn increasing interest in visual tracking owing to its powerfulness in feature extraction. Most existing CNN-based trackers treat tracking as a classification problem. However, these trackers are sensitive to similar distractors because their CNN models mainly focus on inter-class classification. To address this problem, we use self-structure information of...
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