Computer vision for plant and animal inventory

The population, composition, and spatial distribution of the plants and animals in certain regions are always important data for natural resource management, conservation and farming. The traditional ways to acquire such data require human participation. The procedure of data processing by human is usually cumbersome, expensive and time-consuming. Hence the algorithms for automatic animal and plant inventory show their worth and become a hot topic. We propose a series of computer vision methods for automated plant and animal inventory, to recognize, localize, categorize, track and count different objects of interest, including vegetation, trees, fishes and livestock animals. We make use of different sensors, hardware platforms, neural network architectures and pipelines to deal with the varied properties and challenges of these objects. (1) For vegetation analysis, we propose a fast multistage method to estimate the coverage. The reference board is localized based on its edge and texture features. And then a K-means color model of the board is generated. Finally, the vegetation is segmented at pixel level using the color model. The proposed method is robust to lighting condition changes. (2) For tree counting in aerial images, we propose a novel method called density transformer, or DENT, to learn and predict the density of the trees at different positions. DENT uses an efficient multi-receptive field network to extract visual features from different positions. A transformer encoder is applied to filter and transfer useful contextual information across different spatial positions. DENT significantly outperformed the existing state-of-art CNN detectors and regressors on both the dataset built by ourselves and an existing cross-site dataset. (3) We propose a framework of fish classification system using boat cameras. The framework contains two branches. A branch extracts the contextual information from the whole image. The other branch localizes all the individual fish and normalizes their poses. The classification results from the two branches are weighted based on the clearness of the image and the familiarness of the context. Our system achieved the top 1 percent rank in the competition of The Nature Conservancy Fisheries Monitoring. (4) We also propose a video-based pig counting algorithm using an inspection robot. We adopt a novel bottom-up keypoint tracking method and a novel spatial-aware temporal response filtering method to count the pigs. The proposed approach outperformed the other methods and even human competitors in the experiments.Includes bibliographical references

Application of Machine Learning Identification and Classification of Muturu and Keteku Cattle Species for a Smart Agricultural Practice in Developing Countries such as Nigeria

Smart technologies have drastically reshaped the traditional methods of practicing agriculture as witnessed in husbandry. In this paper, a novel application of machine learning identification and classification of Muturu and Keteku cattle species in Nigeria was proposed as the mainstream model that enables the precision and intelligence perception of animal husbandry for a smart agricultural practice using enhanced mask region-based convolutional neural networks (mask R-CNN). A performance accuracy of 0.92 mAP (mean Average Precision) was achieved by the enhanced mask R-CNN model, making it on a par with the existing models

Advances in Object and Activity Detection in Remote Sensing Imagery

The recent revolution in deep learning has enabled considerable development in the fields of object and activity detection. Visual object detection tries to find objects of target classes with precise localisation in an image and assign each object instance a corresponding class label. At the same time, activity recognition aims to determine the actions or activities of an agent or group of agents based on sensor or video observation data. It is a very important and challenging problem to detect, identify, track, and understand the behaviour of objects through images and videos taken by various cameras. Together, objects and their activity recognition in imaging data captured by remote sensing platforms is a highly dynamic and challenging research topic. During the last decade, there has been significant growth in the number of publications in the field of object and activity recognition. In particular, many researchers have proposed application domains to identify objects and their specific behaviours from air and spaceborne imagery. This Special Issue includes papers that explore novel and challenging topics for object and activity detection in remote sensing images and videos acquired by diverse platforms

Activity in area V3A predicts positions of moving objects

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Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words

Head-Driven Phrase Structure Grammar

Head-Driven Phrase Structure Grammar (HPSG) is a constraint-based or declarative approach to linguistic knowledge, which analyses all descriptive levels (phonology, morphology, syntax, semantics, pragmatics) with feature value pairs, structure sharing, and relational constraints. In syntax it assumes that expressions have a single relatively simple constituent structure. This volume provides a state-of-the-art introduction to the framework. Various chapters discuss basic assumptions and formal foundations, describe the evolution of the framework, and go into the details of the main syntactic phenomena. Further chapters are devoted to non-syntactic levels of description. The book also considers related fields and research areas (gesture, sign languages, computational linguistics) and includes chapters comparing HPSG with other frameworks (Lexical Functional Grammar, Categorial Grammar, Construction Grammar, Dependency Grammar, and Minimalism)

Head-Driven Phrase Structure Grammar

Head-Driven Phrase Structure Grammar (HPSG) is a constraint-based or declarative approach to linguistic knowledge, which analyses all descriptive levels (phonology, morphology, syntax, semantics, pragmatics) with feature value pairs, structure sharing, and relational constraints. In syntax it assumes that expressions have a single relatively simple constituent structure. This volume provides a state-of-the-art introduction to the framework. Various chapters discuss basic assumptions and formal foundations, describe the evolution of the framework, and go into the details of the main syntactic phenomena. Further chapters are devoted to non-syntactic levels of description. The book also considers related fields and research areas (gesture, sign languages, computational linguistics) and includes chapters comparing HPSG with other frameworks (Lexical Functional Grammar, Categorial Grammar, Construction Grammar, Dependency Grammar, and Minimalism)

XXIII Congreso Argentino de Ciencias de la Computación - CACIC 2017 : Libro de actas

Trabajos presentados en el XXIII Congreso Argentino de Ciencias de la Computación (CACIC), celebrado en la ciudad de La Plata los días 9 al 13 de octubre de 2017, organizado por la Red de Universidades con Carreras en Informática (RedUNCI) y la Facultad de Informática de la Universidad Nacional de La Plata (UNLP).Red de Universidades con Carreras en Informática (RedUNCI