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This paper presents a general framework for live detection of broilers in poultry houses. The challenges for image recognition of broilers are posted by crowded scenes, poor image quality and difficulty in acquiring a benchmark of labeled samples. The proposed framework consists on the use of image thresholding, morphological transformations, feature engineering, in addition to supervised and unsupervised...
This paper presents an automated computer vision system of shape defect detection for product quality inspection and monitoring system. Soft drink bottle is used as a tested product for the proposed system. The analysis framework includes data collection, pre-processing, morphological operation, feature extraction, and classification. Morphological operation technique is used to segment the image...
Synthetic Aperture Radar (SAR) data is very important to land cover change detection, mainly in areas of tropical forests that are constantly under cloud cover. In this study, six features extracted from two L band SAR full-polarimetric images were evaluated for region based binary change detection in a region within the Brazilian Amazon, in the years of 2006 and 2009. These features were the intensities...
Oil spills present a major threat to the sea ecosystem and thus need to be monitored on a regular basis. Synthetic Aperture Radar (SAR) data is well known for ocean monitoring capabilities. SENTINEL 1 (SEN1) extra wide (EW) mode data and RADARSAT-2 (RS2) Maritime Satellite Surveillance Radar (MSSR) modes have been developed to further improve ocean surveillance. This data can monitor large areas (400...
Effective and cost-efficient monitoring is indispensable for ensuring environmental sustainability. Cyanobacterial Harmful Algal Blooms (CyanoHABs) are a major water quality and public health issue in inland water bodies. The recent popularity of online social media (OSM) platforms coupled with advances in cloud computing and data analytics has given rise to citizen science-based approaches to environmental...
Vision based environmental monitoring using fixed cameras generates large image collections, creating a bottleneck in data analysis. In areas with limited background knowledge of the monitored habitat, this bottleneck can often not be overcome by traditional pattern recognition methods. A new change detection method to identify interesting events such as presence and behavior of different species...
In recent years, a number of fixed long-term underwater observatories (FUO) have been deployed to monitor marine habitats over time. HD cameras deployed on FUOs enable vision based studies of long-term processes in the monitored habitats. However, in many marine environments there is often only little a-priori knowledge about potential changes that can be expected or where such changes are likely...
In this paper we introduce an automatic monitoring system for the detection and the evaluation of the evolution of hemangiomas using a fuzzy logic system based on two parameters: area and redness. We have considered pairs of images (from two different moments in time) that show hemangiomas either evolving, stationary or regressing. The starting points of the algorithm are the rectangular regions of...
Fixed underwater observatories (FUOs) equipped with a variety of sensors including HD cameras, allow long-term monitoring with a high temporal resolution of a limited area of interest. FUOs enable in situ monitoring of visual features like size and color of for instance live cold-water corals using imaging techniques. We present a computational workflow to extract coral features from the huge collection...
As the development of marine economy and population explosion, coastal areas is suffering great pressure - because of the immigration from inland to the developed cities along east China. Island coastal zones, which is a specific ecosystem surrounded by the sea, is more sensitive to human activities, e.g. reclamations. It is essential to monitor the dynamic changes of the island coastal areas to retrieve...
The paper concerns the detection of fall events based on human silhouette shape variations. The detection of fall events is addressed from the statistical point of view as an anomaly detection problem. Specifically, the paper investigates the multivariate exponentially weighted moving average (MEWMA) control chart to detect fall events. Towards this end, a set of ratios for five partial occupancy...
This paper presents a tensor voting approach to automated detection of dark spots in RADARSAT-1 ScanSAR Narrow Beam mode images. First, a thresholding algorithm that well maximizes the ratio of between-class variance to within-class variance is used to detect potential dark spot candidates. Next, a tensor voting framework integrated with sparse and dense ball votings is carried out to suppress noise...
Since sand under water occurs siltation and leads to clogging issue, it has become a serious security problem to ships in inner-river. Therefore, it is very important to lay mattress in waterway to cure sediment and ensure the normal depth. However, due to the underwater flow speed, laying mattress is very difficult. The Mattress is often in offset position, and this could result in exposing riverbed...
Many studies shows that quality of agricultural products may be reduced from many causes. One of the most important factors contributing to low yield is disease attack. The plant disease such as fungi, bacteria and viruses. The leaf disease completely destroys the quality of the leaf. Common ground nut disease is cercospora. It is one of the type of disease in early stage of ground nut leaf. The upgraded...
Identification of the plant diseases is the key to preventing the losses in the yield and quantity of the agricultural product. The studies of the plant diseases mean the studies of visually observable patterns seen on the plant. Health monitoring and disease detection on plant is very critical for sustainable agriculture. It is very difficult to monitor the plant diseases manually. It requires tremendous...
This paper introduces Bayesian approach for automated delineation of meningioma brain tumor using post contrast T1 weighted magnetic resonance image. The proposed framework follows the basis of pixel based classification, combination of two stages; feature extraction followed by learning and classification of pixels into desired classes. Both intensity and texture features are extracted. Thereafter,...
We present a method for improving human segmentation results in calibrated, multi-view environments using features derived from both pixel (image) and voxel (volume) space. The main focus of this work is to develop a low-cost, vision-based system for passive activity monitoring of older adults in the home, to capture early signs of illness and functional decline and allow seniors to live independently...
Unmanned airship-based remote sensing is widely used in agriculture, remote sensing, environmental monitoring and detection of military camouflage works. A multi-spectral remote sensing image mosaic technique with SIFT feature matching is proposed to deal with images from weak wind, poor stability unmanned airship. Firstly, the characteristics of multi-spectrum image from unmanned airship are analyzed...
In this paper, a framework for recognition of Bangla ticker text1 from the Bangla news videos is presented. Tesseract OCR [1] has been used for Bangla script recognition. Tesseract OCR gives good results for text recognition in documents. But in case of images and videos, some processing is required beforehand. Approach here is to provide processed images to the Tesseract OCR to get better results...
This paper presents a dynamical decision method derived from ensemble decision method. It is designed to be robust with respect to abrupt change of sensor response. Abrupt change may be caused by impulsive noise, sensor degradation or transmission fault in the case of an autonomous sensor network. It can also be caused by inconsistency of sensor responses due to local or sudden break of one monitored...
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