Advanced Techniques of Foreground, Background and Object Identification in Video Application

Volume 7 Issue 10 (October 201

Exploration and Comparison of Image-based Techniques for Strawberry Detection

Strawberry is an important cash crop in California, and its supply accounts for 80% of the US market [2]. However, in current practice, strawberries are picked manually, which is very labor-intensive and time-consuming. In addition, the farmers need to hire an appropriate number of laborers to harvest the berries based on the estimated volume. When overestimating the yield, it will cause a waste of human resources, while underestimating the yield will cause the loss of the strawberry harvest [3]. Therefore, accurately estimating harvest volume in the field is important to farmers. This paper focuses on an image-based solution to detect strawberries in the field by using the traditional computer vision technique and deep learning method. When strawberries are in different growth stages, there are considerable differences in their color. Therefore, various color spaces are first studied in this work, and the most effective color components are used in detecting strawberries and differentiating mature and immature strawberries. In some color channels such as the R color channel from the RGB color model, Hue color channel from the HSV color model, \u27a\u27 color channel from the Lab color model, the pixels belonging to ripe strawberries are clearly distinguished from the background pixels. Thus, the color-based K-mean cluster algorithm to detect red strawberries will be exploited. Finally, it achieves a 90.5% truth-positive rate for detecting red strawberries. For detecting the unripe strawberry, this thesis first trained the Support Vector Machine classifier based on the HOG feature. After optimizing the classifier through hard negative mining, the truth-positive rate reached 81.11%. Finally, when exploring the deep learning model, two detectors based on different pre-trained models were trained using TensorFlow Object Detection API with the acceleration of Amazon Web Services\u27 GPU instance. When detecting in a single strawberry plant image, they have achieved truth-positive rates of 89.2% and 92.3%, respectively; while in the strawberry field image with multiple plants, they have reached 85.5% and 86.3%

A hybrid approach for stain normalisation in digital histopathological images

Stain in-homogeneity adversely affects segmentation and quantifi-cation of tissues in histology images. Stain normalisation techniques have been used to standardise the appearance of images. However, most the available stain normalisation techniques only work on a particular kind of stain images. In addition, some of these techniques fail to utilise both the spatial and tex-tural information in histology images, leading to image tissue distortion. In this paper, a hybrid approach has been developed, based on an octree colour quantisation algorithm combined with the Beer-Lambert law, a modified blind source separation algorithm, and a modified colour transfer approach. The hybrid method consists of two stages the stain separation stage and colour transfer stage. An octree colour quantisation algorithm combined with Beer-Lambert law, and a modified blind source separation algorithm are used during the stain separation stage to computationally estimate the amount of stain in an histology image based on its chromatic and luminous response. A modified colour transfer algorithm is used during the colour transfer stage to minimise the effect of varying staining and illumination. The hybrid method addresses the colour variation problem in both H&DAB (Haemotoxylin and Diaminoben-zidine) and H&E (Haemotoxylin and Eosin) stain images. The stain normali-sation method is validated against ground truth data. It is widely known that the Beer-Lambert law applies to only stains (such as haematoxylin, eosin) that absorb light. We demonstrate that the Beer-Lambert law applies is applicable to images containing a DAB stain. Better stain normalisation results are obtained in both H&E and H&DAB images

Identificação automática dos estágios de vida da Bemisia tabaci spp. por visão computacional e aprendizagem de máquina

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2019.Pragas agrícolas têm sofrido um aumento de incidência nas últimas décadas. Pesticidas como a maior forma de controle de pragas tem declinado em eficiência, na medida que a resistência das pragas aumenta. Métodos de controle mais naturais tem sido testados com grande sucesso em controlar estas populações de pragas. Tais métodos se beneficiam e necessitam de informações sobre essas pragas. Neste trabalho é apresentada uma metodologia de reconhecimento e contagem da mosca branca (Bemisia tabaci spp) em 6 diferentes estágios de seu ciclo de vida usando modelo de Aprendizagem de Máquina, Random Forests e regras morfologicamente derivadas. Fornecidas pela Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) 240 imagens foram utilizadas, obtidas com câmera de espectro comum. As imagens foram rotuladas e cada uma comparada com as regras entomológicas para a contagem e classificação de cada objeto: exúvia, ínstar 1 ao ínstar 4 e a mosca adulta. O sistema foi treinado em 100 imagens e performou a mesma tarefa de classificação nas 140 imagens restantes previamente não vistas pelo sistema e os resultados comparados. Os resultados mostraram acurácia de 95% de classificação por pixel e 86% para a classificação dos objetos. Sendo essa metodologia compatível com métodos similares.Agricultural pests have been increasing in the past few decades. Pesticides as the major control method is declining in efficiency as pest resistance is increasing. More natural methods have been tested with great success in controlling pest population, such methods require and benefits with more information about the pests. Our work is to present a methodology for the recognition and counting of the silverleaf whitefly (Bemisia tabaci spp) in 6 different stages of its life cycle using the Machine Learning model, Random Forests and morphological derived rules. Given by Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA), 240 images were used, taken with a consumer entry level camera. The images were labeled and each one compared to entomological rules to count and classify each object: exuviae, instar 1 to 4 and adult flies. The system was trained in 100 images and performed the same classification task in the remaining unseen 140 images and the results compared. The results showed accuracy of 95% for pixel classification and 86% for object classification. Being this methodology comparable with similar methods