H.5. Image Processing and Computer Vision
Pouria Maleki; Abbas Ramazani; Hassan Khotanlou; Sina Ojaghi
Abstract
Providing a dataset with a suitable volume and high accuracy for training deep neural networks is considered to be one of the basic requirements in that a suitable dataset in terms of the number and quality of images and labeling accuracy can have a great impact on the output accuracy of the trained ...
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Providing a dataset with a suitable volume and high accuracy for training deep neural networks is considered to be one of the basic requirements in that a suitable dataset in terms of the number and quality of images and labeling accuracy can have a great impact on the output accuracy of the trained network. The dataset presented in this article contains 3000 images downloaded from online Iranian car sales companies, including Divar and Bama sites, which are manually labeled in three classes: car, truck, and bus. The labels are in the form of 5765 bounding boxes, which characterize the vehicles in the image with high accuracy, ultimately resulting in a unique dataset that is made available for public use.The YOLOv8s algorithm, trained on this dataset, achieves an impressive final precision of 91.7% for validation images. The Mean Average Precision (mAP) at a 50% threshold is recorded at 92.6%. This precision is considered suitable for city vehicle detection networks. Notably, when comparing the YOLOv8s algorithm trained with this dataset to YOLOv8s trained with the COCO dataset, there is a remarkable 10% increase in mAP at 50% and an approximately 22% improvement in the mAP range of 50% to 95%.
Seyedeh S. Sadeghi; H. Khotanlou; M. Rasekh Mahand
Abstract
In the modern age, written sources are rapidly increasing. A growing number of these data are related to the texts containing the feelings and opinions of the users. Thus, reviewing and analyzing of emotional texts have received a particular attention in recent years. A System which is based on combination ...
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In the modern age, written sources are rapidly increasing. A growing number of these data are related to the texts containing the feelings and opinions of the users. Thus, reviewing and analyzing of emotional texts have received a particular attention in recent years. A System which is based on combination of cognitive features and deep neural network, Gated Recurrent Unit has been proposed in this paper. Five basic emotions used in this approach are: anger, happiness, sadness, surprise and fear. A total of 23,000 Persian documents by the average length of 24 have been labeled for this research. Emotional constructions, emotional keywords, and emotional POS are the basic cognitive features used in this approach. On the other hand, after preprocessing the texts, words of normalized text have been embedded by Word2Vec technique. Then, a deep learning approach has been done based on this embedded data. Finally, classification algorithms such as Naïve Bayes, decision tree, and support vector machines were used to classify emotions based on concatenation of defined cognitive features, and deep learning features. 10-fold cross validation has been used to evaluate the performance of the proposed system. Experimental results show the proposed system achieved the accuracy of 97%. Result of proposed system shows the improvement of several percent’s in comparison by other results achieved GRU and cognitive features in isolation. At the end, studying other statistical features and improving these cognitive features in more details can affect the results.
H.5. Image Processing and Computer Vision
M. Shakeri; M.H. Dezfoulian; H. Khotanlou
Abstract
Histogram Equalization technique is one of the basic methods in image contrast enhancement. Using this method, in the case of images with uniform gray levels (with narrow histogram), causes loss of image detail and the natural look of the image. To overcome this problem and to have a better image contrast ...
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Histogram Equalization technique is one of the basic methods in image contrast enhancement. Using this method, in the case of images with uniform gray levels (with narrow histogram), causes loss of image detail and the natural look of the image. To overcome this problem and to have a better image contrast enhancement, a new two-step method was proposed. In the first step, the image histogram is partitioned into some sub-histograms according to mean value and standard deviation, which will be controlled with PSNR measure. In the second step, each sub-histogram will be improved separately and locally with traditional histogram equalization. Finally, all sub-histograms will be combined to obtain the enhanced image. Experimental results shows that this method would not only keep the visual details of the histogram, but also enhance image contrast.
