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Trichromacy might have evolved in primates for the purpose of emotion recognition [1]. Emotional states change the quantity of blood and its oxygenation under the skin, which cause subtle changes in skin color to become visible, especially in the face. We manipulated photos of basic emotional expressions so that skin color was either congruent or incongruent (according to [1]) with the expression...
A multilevel halftoning algorithm can be used to overcome some of the challenges of multi-channel printing. In this algorithm, each channel is processed so that it can be printed using multiple inks of approximately the same hue, achieving a single ink layer. The computation of the threshold values required for ink separation and dot gain compensation pose an interesting challenge. Since the dot gain...
Histograms of Oriented Gradients (HOG) feature has been successfully used in pedestrian detection and achieves high accuracy. This paper introduces a content retrieval algorithm based on improved HOG. The method has two steps which are adjusting the HOG structure by scanning the image with a sliding HOG window and reducing feature dimension by principle component analysis (PCA) technique. The experimental...
This paper proposes a novel method for land cover area frame stratification based on corn planting frequency and percent cultivation. South Dakota U.S. geospatial crop frequency (2008–2013) and cultivation (2013) data layers created from NASS Cropland Data Layers are utilized to develop a novel area sampling frame (ASF) stratification design. Eight corn planting frequency strata are derived using...
In the past SAR data has been proven as a great source for land cover characterization. For classification purpose many individual methods has been used, but single method are likely to undergo high variance or biasness depending on the base used for classification. Hence, in this paper random forest classification technique has been used for SAR data classification into different land cover classes...
Human action recognition system is fundamental of human activity and behavior recognition, especially for video analysis technologies. In this paper, we introduce an improvement method for human action recognition proposed by P.Chawalitsittikul et al. The actions from RGBD multi-views, taken from cameras at different static-viewpoints in the overlapping Area of Interest, are fused at high-level decision...
A new region-based local stereo matching algorithm with accurate disparity estimation is proposed. For the local stereo matching, finding an appropriate support window is crucial to the performance of disparity estimation. In order to generate an accurate support region, a modified cross-based local approach combined with mean-shift segmentation is performed. We then further improve the reliability...
The precision of visual matching and the trade-off between accuracy and time efficiency have long been bottlenecks of image search systems. This work addresses the two problem simultaneously by introducing the coupled Multi-Index (cMI) structure. First, by combining SIFT and color features on the indexing-level, the discriminative power of visual words is greatly enhanced. Second, by reducing the...
Hand gestures are used widely in communication. An important example is using in the sign languages. Many hand gesture silhouettes are the part of other hand gesture silhouettes. For example, V sign gesture is a part of the high five gesture, because we can create high five gesture silhouettes from the V sign gesture silhouettes by extending the other three fingers. Here we propose the partial contour...
Classification of large amount of images calls for diverse types of features, but employing all possible feature types will create unnecessary computation burden, and may result in reduced classification accuracy. Selecting feature vectors individually is not a feasible solution in this scenario due to the high amount of feature vectors needed for reasonable performance. Instead, this paper proposes...
This study proposes an Intelligent Tutor System for assessing slide presentations from novice undergraduate students. To develop such system, two learner models (rule based model and clustering model) were built using 80 presentations graded by three human experts. An experiment to determine the best learner model and students' perception was carried out using 51 presentations uploaded by students...
Object detection is one of the most interesting branches in computer vision. Accurate detection systems can be utilized to various areas. There are two steps in detection, feature extraction and classification. In this paper, new feature extraction method is proposed. Histogram Oriented Gradient (HOG) is famous, fast and accurate feature, but it is not rotation invariant. This paper proposes a new...
Image registration for stack-based HDR photography is challenging. If not properly accounted for, camera motion and scene changes result in artifacts in the composite image. Unfortunately, existing methods to address this problem are either accurate, but too slow for mobile devices, or fast, but prone to failing. We propose a method that fills this void: our approach is extremely fast—under 700ms...
We propose a novel approach to segment hand regions in egocentric video that requires no manual labeling of training samples. The user wearing a head-mounted camera is prompted to perform a simple gesture during an initial calibration step. A combination of color and motion analysis that exploits knowledge of the expected gesture is applied on the calibration video frames to automatically label hand...
Assessment of food intake has a wide range of applications in public health and life-style related chronic disease management. In this paper, we propose a real-time food recognition platform combined with daily activity and energy expenditure estimation. In the proposed method, food recognition is based on hierarchical classification using multiple visual cues, supported by efficient software implementation...
In this paper, we evaluate the generalization power of deep features (ConvNets) in two new scenarios: aerial and remote sensing image classification. We evaluate experimentally ConvNets trained for recognizing everyday objects for the classification of aerial and remote sensing images. ConvNets obtained the best results for aerial images, while for remote sensing, they performed well but were outperformed...
In this paper, we examined the effectiveness of deep convolutional neural network (DCNN) for food photo recognition task. Food recognition is a kind of fine-grained visual recognition which is relatively harder problem than conventional image recognition. To tackle this problem, we sought the best combination of DCNN-related techniques such as pre-training with the large-scale ImageNet data, fine-tuning...
In this paper, a clothes segmentation method for fashion parsing is described. This method does not rely in a previous pose estimation but people segmentation. Therefore, novel and classic segmentation techniques have been considered and improved in order to achieve accurate people segmentation. Unlike other methods described in the literature, the output is the bounding box and the predominant color...
Microsoft Kinect had a key role in the development of consumer depth sensors being the device that brought depth acquisition to the mass market. Despite the success of this sensor, with the introduction of the second generation, Microsoft has completely changed the technology behind the sensor from structured light to Time-Of-Flight. This paper presents a comparison of the data provided by the first...
Convolutional Neural Network (CNN) is efficient in learning hierarchical features from large image datasets, but its model complexity and large memory foot prints are preventing it from being deployed to devices without a server back-end support. Modern CNNs are always trained on GPUs or even GPU clusters with high speed computation capability due to the immense size of the network. A device based...
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