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Timely and robust diagnosis of plant diseases and nutrient deficiencies play a major role in management of crop yield. Automation is a low cost alternative to human experts and can help to detect early onset of crop diseases which aids faster decision making and in giving recommendations to farmers to curb yield loss. We have developed a smart-phone based participatory sensing application for agriculture...
Mobile device features such as camera and other sensors are evolving rapidly nowadays. Supported by a reliable communications network, it raises new methods in information retrieval. Mobile devices can capture an image with its camera and pass it to the retrieval systems to get the information needed. This system, called Mobile Content-Based Image Retrieval (MCBIR), generally consists of two parts:...
The goal of image quality assessment (IQA) is to use computational models to measure the consistency between image quality and subjective evaluations. In recent years, convolutional neural networks (CNNs) have been widely used in image processing community and have achieved performance leaps than non CNNs-based methods. In this work, we describe an accurate deep CNNs model for no-reference IQA. Taking...
A great amount of data is usually needed for a recommender system to learn the associations between users and items. However, in practical applications, new users and new items emerge everyday, and the system has to react to them promptly. The ability to recommend proper items to new users affects the users' first impression and accordingly the retention rate, whereas recommending new items to proper...
In this paper, we propose a discriminative keypoint selection-based 3D face recognition method that is superior to prevalent techniques in terms of both computational complexity and performance. We use the average face model (AFM) for face registration to efficiently locate the axis of symmetry in the rotated face mesh and recover a full frontal face from a 3D face model commonly corrupted due to...
The performance of a gait recognition system is very much related to the usage of efficient feature representation and recognition modules. The first extracts features from an input image sequence to represent a user's distinctive gait pattern. The recognition module then compares the features of a probe user with those registered in the gallery database. This paper presents a novel gait feature representation,...
Saliency detection aims to focus attention on the important parts of a map, which is an excellent ability of human visual system. In this paper, we present a saliency detection model based on the principle that the pixels belong to the background are more disperse than the ones of the target area. Color contrast in different channels is employed to classify the pixels. Our method outperformed five...
Recent research on machine learning focuses on audio source identification in complex environments. They rely on extracting features from audio signals and use machine learning techniques to model the sound classes. However, such techniques are often not optimized for a real-time implementation and in multi-source conditions. We propose a new real-time audio single-source classification method based...
Model-based approaches to Speaker Verification (SV), such as Joint Factor Analysis (JFA), i-vector and relevance Maximum-a-Posteriori (MAP), have shown to provide state-of-the-art performance for text-dependent systems with fixed phrases. The performance of i-vector and JFA models has been further enhanced by estimating posteriors from Deep Neural Network (DNN) instead of Gaussian Mixture Model (GMM)...
The inherent dependencies among video content, personal characteristics, and perceptual emotion are crucial for personalized video emotion tagging, but have not been thoroughly exploited. To address this, we propose a novel topic model to capture such inherent dependencies. We assume that there are several potential human factors, or “topics,” that affect the personal characteristics and the personalized...
Now-a-days, providing a platform, where customers can obtain first-hand assessments of product information and manufacturers can collect customers reviews cum feedbacks to improve the product quality, is the prime need of any organization. These reviews are the people's opinion about particular product. Every website has reviews of their product and those reviews are difficult to analyze. Thus, the...
We propose a novel geometric framework for analyzing spontaneous facial expressions, with the specific goal of comparing, matching, and averaging the shapes of landmarks trajectories. Here we represent facial expressions by the motion of the landmarks across the time. The trajectories are represented by curves. We use elastic shape analysis of these curves to develop a Riemannian framework for analyzing...
Walking speed change is one of the most common cofactor that affects the gait signature. The intra-class variations increases by the changes of walking speed and the training data set fails to model the variations. As a consequence, the performance of the gait recognition systems is degraded. Naturally, human reconstruct the model by considering the unaffected body parts to recognize a person when...
This work focuses on Emirati speaker verification systems in neutral talking environments based on each of First-Order Hidden Markov Models (HMMls), Second-Order Hidden Markov Models (HMM2s), and Third-Order Hidden Markov Models (HMM3s) as classifiers. These systems have been evaluated on our collected Emirati speech database which is comprised of 25 male and 25 female Emirati speakers using Mel-Frequency...
Initialization and feature selection are crucial in supervised landmark detection. Mismatching caused by detector error and discrepant initialization is very common in these existing methods. To solve this problem, we proposed a new method, which includes a new initialize model and multitask feature learning, for the robust facial landmark localization. In our new method, firstly, a fast detection...
Virtual screening enables to search large small-molecule compound libraries for active molecules with respect to given macromolecular target. In ligand-based virtual screening, this goal is achieved by utilizing information about fragments or patterns present in existing known active compounds. Typically, the patterns are encoded as fingerprints which are used to screen a database of candidate compounds...
We present a progressive method of applying easy-to-hard grouping technique that applies increasingly sophisticated feature descriptors and classifiers on reducing number of image samples from each of the iteratively generated clusters. The primary goal of the proposed approach is to design a cost effective face clustering method to deploy on low-power devices like Mobile phones, while handling various...
Face detection has been a hotspot either in research and in commercial application. In this paper, Locally Assembled Binary (LAB) feature and Adaboost algorithm are combined to recognize human face in images. On the basis of ensuring the detection speed, the detection accuracy is improved. Integral image technology is also conducted in consideration of detection speed. The proposed method is tested...
Given a child's and a couple's facial photos, tri-subject kinship verification aims to determine the existence of blood relation between the child and the couple. Different from existing methods which model the kinship inheritance process among three persons in separate stages and only use simple features, this work establishes a simple model inspired by genetics to measure tri-subject kinship similarity...
Saliency models provide heatmaps highlighting the probability of each pixel to attract human gaze. To define image's important regions, features maps are extracted. The rarity, surprise or contrast are computed leading to conspicuity maps, showing important regions of each feature map. The final saliency map is obtained by merging these maps. The fusion process is usually a linear combination of the...
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