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Image classification is one of the critical tasks in hyperspectral remote sensing. In recent years, significant improvement have been achieved by various classification methods. However, mixed spectral responses from different ground materials still create confusions in complex scenes. In this regard, unmixing approaches are being successfully carried out to decompose mixed pixels into a collection...
Object deformation and occlusion are ubiquitous problems for visual tracking. Though many efforts have been made to handle object deformation and occlusion, most existing tracking algorithms fail in case of large deformation and severe occlusion. In this paper, we propose a graph learning-based tracking framework to handle both challenges. For each consecutive frame pair, we construct a weighted graph,...
Image smoothing is a fundamental technology which aims to preserve image structure and remove insignificant texture. Balancing the trade-off between preserving structure and suppressing texture, however, is not a trivial task. This is because existing methods rely on only one guidance to infer structure or texture and assume the other is dependent. However, in many cases, textures are composed of...
Region-based image retrieval has been proven to be effective in finding relevant images. In this paper, we propose a cuboid im-age segmentation method which results in rectangle image partitions. Rectangle partitions are more suitable for image compression, retrieval and other image operations. We apply partitions in image retrieval in this paper. Our experimental results have shown that (1) the proposed...
Using color histograms in automatic emotion recognition systems faces different issues. One of the important challenges is to determine the appropriate number of bins in the color histogram to achieve the highest recognition performance possible with minimal computations. This research focuses on emotion recognition induced by visual contents of images, or REVC for short, using ARTphoto dataset. Twenty-two...
Using spatio-temporal features is popular for action recognition. However, existing methods embed these local features into a global representation. Orders and correlations among local motions of each action are missing. This can make it difficult to distinguish closely related actions. This paper proposes a solution to address this challenge by encoding correlations of movements. Space-time interest...
Remote sensing image registration is still a challenging task because of diverse image types and the lack of a consistent transformation. To improve image registration in remote sensing, this paper develops a robust and accurate feature point matching framework. A modified scale-invariant feature transform (SIFT) method is first introduced for feature detection and pair matching. Based on the properties...
This paper presents a novel local posture orientation-context descriptor, and proposes a FDDL(Fisher discriminant dictionary learning) method based on local orientation-preserving(LOP-FDDL) for sparse coding in action recognition task. To take full use of the information about the position of the local body-part related to the center of the torso, ant the spatial-temporal shape changes of the human...
The traditional procedure for the building change detection is subjective to user's knowledge of the involved data. The complexity increases further due to unavailability of a public data set that is scanned on two different dates. Therefore, the manual changes in the reference data are more common to generate modified data. This paper first presents a strategy to introduce five types of changes in...
Multi-object model-free tracking is challenging because the tracker is not aware of the objects' type (not allowed to use object detectors), and needs to distinguish one object from background as well as other similar objects. Most existing methods keep updating their appearance model individually for each target, and their performance is hampered by sudden appearance change and/or occlusion. We propose...
This paper presents a new calibration method for lenslet-based plenoptic cameras. While most existing approaches require the computation of sub-aperture images or depth maps which quality depends on some calibration parameters, the proposed process uses the raw image directly. We detect micro-images containing checkerboard corners and use a pattern registration method to estimate their positions with...
Morse code is one of the earliest means of telecommunications; however, it is rarely used nowadays due to viral mobile communications. Although a person can tap Morse codes using his/her fingers easily, perhaps nobody is aware of this kind of finger gestures anymore. In this paper, we will develop a prototype combined together the principle of old Morse code with finger gesture recognition in digital...
Hand-shape recognition is an important problem in computer vision with significant societal impact. In this work, we introduce a new image dataset for Irish Sign Language (ISL) recognition and we compare between two recognition approaches. The dataset was collected by filming human subjects performing ISL hand-shapes and movements. Then, we extracted frames from the videos. This produced a total of...
Color models are widely used in image recognition because they represent significant information. On the other hand, texture analysis techniques have been extensively used for facial feature extraction. In this paper, we extract discriminative features related to facial attributes by utilizing different color models and texture analysis techniques. Specifically, we propose novel methods for texture...
In this study, we introduce an ensemble-based approach for online machine learning. Here, instead of working on the original data, several Hoeffding tree classifiers classify and are updated on the lower dimensional projected data generated from originality by random projections. Since random projection is unstable, from one example, many diverse training data can be created to train the set of Hoeffding...
The maximum consensus problem lies at the core of several important computer vision applications as it is one of the most popular criteria for robust estimation. Although considerable efforts have been devoted to solving this problem, exact algorithms are still impractical for real-world data. Randomized hypothesize-and-test approaches such as RANSAC and its variants are therefore still the key players...
We present a method that integrates a part-based sparse appearance model in a Bayesian inference framework for tracking targets in video sequences. We formulate the sparse appearance model as a set of smoothed colour histograms corresponding to the object windows detected by the Deformable Part Model (DPM) detector. The data association of each body part between frames is solved based on the position...
This paper proposes a new approach for analysing crowded video scenes. The proposed approach decomposes the scene motion dynamics into a graph of interconnected atomic elements of coherent motions named Motion Units (MUs). Different MUs cover scene's local regions with different size and shape, which can even overlap. MUs relationships are analysed to discover the scene entrances and exits. Dominant...
Numerous style transfer methods which produce artistic styles of portraits have been proposed to date. However, the inverse problem of converting the stylized portraits back into realistic faces is yet to be investigated thoroughly. Reverting an artistic portrait to its original photo- realistic face image has potential to facilitate human perception and identity analysis. In this paper, we propose...
Digital and paper based documents co-exist in our daily lives. Seamless integration of information from both sources is crucial for efficient knowledge management. This paper address the algorithm that can handle the detection of document so that it can be captured easily to convert it into a digital form for automatic integration of relevant information in electronic work flows. It uses the deep...
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