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Millimeter Wave Doughnut Slot MIMO Antenna

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Description

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The project presents logo characteristics analysis usingimage processing ? ? techniques for automated vision system used at agricultural field.? In agriculture research of automatic logo characteristics detection is essential one in monitoring large fields of crops, and thus automatically detects symptoms oflogo characteristics as soon as they appear on plant leaves. The proposed decision making system utilizes image content characterization and supervised classifier type of neural network. Image processing techniques for this kind of decision analysis involves preprocessing, feature extraction and classification stage. At Processing, an input image will be resized and region of interest selection performed if needed. Here, color and texture features are extracted from an input for network training and classification. Color features like mean, standard deviation of HSV color space and texture features like energy, contrast, homogeneity and correlation. The system will be used to classify the test images automatically to decide logo characteristics. For this approach, automatic classifier NN be used for classification based on learning with some training samples of that some category. This network uses tangent sigmoid function as kernel function. Finally, the simulated result shows that used network classifier provides minimum error during training and better accuracy in classification.

Existing method

Most of the existing methods are based on connected component analysis (CCA) and doesn?t work in realtime due to many customized processing steps involved. Hence, considering Deep CNNs is quite intuitive, given the recent success in basic computer vision problems.

PROPOSED method

This paper investigates the possibility of modelling the signature and logo detection task as a end-to-end object detection problem

In this paper, we proposed to use the state-of-the-art Deep Convolutional Neural Networks (CNN) for detecting signatures and logos from scanned documents. Specifically, we analyze the potential of Faster-RCNN [17] and YOLOv2 [24] for the detection of the areas-of-interest and adapt it to the document retrieval problem. Four different network architectures namely ZF [18], VGG16 [19], VGG16-M-1024 [19] and Yolov2 [24] are used in this study. The primary intention is to explore and model the signaure and logo detection task into a standard object detection problem. Additionally, real-time detection of signatures and logos in a single pipeline, makes it more applicable to document retrieval.

Methodologies

  • Preprocessing
  • Color Space Conversion
  • Color and Texture Features Extraction
  • NN classifier

Drawback

  • A major drawback of this technique is that a priori information about the location of the signature is assumed. Ahmed et al. [1] proposed a Speeded Up Robust Features (SURF) based approach for signature segmentation from document images

Application

  • Real-time detection of signatures and logos in a single pipeline, makes it more applicable to document retrieval.

SOFTWARE REQUIREMENT

PYTHON ABOVE 3.0

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