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Agro export industries generate a substantial amount of revenue to Indian economy. In the fruit industry, various fruits like banana, mango, apple and pomegranate, etc. are transported in the conveyor for a post harvest process like classification, sorting, grading and juice extraction. The manual discrimination of various fruits consumes time and, it can be automated. This research work is intended to build an image processing algorithm that ensures automatic discrimination of banana (Musa Species.) from other fruits like Citrus, Apple, and Pomegranate. The input object is segmented using Background subtraction and threshold method. Morphological operations are performed to obtain the clear contour of the segmented objects. The shape of the banana and non-banana are described by scale and translation invariant signature. Binary SVM with signature feature vectors detect the banana fruit from the non-banana fruit automatically. The accuracy rate is 95%.

Visual quality enhancement plays a vital role in low cost imaging systems, machine vision, industrial applications, remote sensing, face recognition systems and medical image interpretation etc. Growth of low cost image processing applications require image preprocessing which enhances details of an image. Most of the contrast enhancement papers apply desired contrast enhancement technique directly to enhance the given input image having poor contrast or contrast at any other undesired level. It is important to predict whether the contrast enhancement is needed for an image, to avoid the artifacts due to enhancement on the good image. In this paper, an algorithm to model images using its local contrast measure has been proposed, to classify and distinguish between the images having different contrast level. The input image is classified either as low contrast or high contrast image using the model. If the classified image is low contrast it will be enhanced using the Stochastic Resonance principle. The results show that the proposed automated procedure enhances the low contrast image better than the conventional enhancement methods.

Object classification is a major application in video surveillance such as automatic vehicle detection and pedestrian detection, which is to monitor thousands of vehicles and people. In this study, an object classification algorithm is proposed to classify the objects into persons and vehicles despite the presence of shadow and partial occlusion in mid-field video using recurrent motion image (RMI) of skeleton features. In this framework, the background subtraction using a Gaussian mixture model is followed by Gabor filter based shadow removal in order to remove the shadow in the image. The star skeletonisation algorithm is performed on the segmented objects to obtain skeleton features. Then the RMI is computed and it is partitioned into two sections such as top and bottom. Based on the signatures derived from the bottom section of the partitioned RMI using skeleton features, the object is classified into people and vehicles.

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