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Intra-class recognition of fruits using image processing and pattern recognition techniques, is a challenging task mainly because sub-types of the same fruit show a large amount of similarities between each other and hence more difficult to distinguish than when different types of fruits are involved (inter-class). The problem becomes more acute when the camera viewpoint also changes which tend to change the known characteristics of the fruits like contour shape. To solve this problem, this paper proposes a view point invariant solution for intra-class recognition of fruits by combining color and texture features and using a Neural Network (NN) classifier. Experimentations done on a dataset of 270 fruit images show satisfactory performance across different fruit types and sub-types.

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Interactive online video applications, such as video telephony, are known for their vulnerability to network condition. With the increasing usage of hand-held wireless mobile devices, which are capable of capturing and processing good quality videos, combined with the flexibility in an end-user movements have added new challenging factors for application providers and network operators. These factors affect the perceived video quality of mobile video telephony applications, unlike conventional video telephony over desktop computers. We investigate this impact on video quality of mobile video telephony in varying network conditions and end-users movement scenarios. Based on 312 live traces, we quantitatively derive the correlation between the perceived video quality and the network Quality of Service (QoS) and user mobility. With the results, we develop a Quality of Experience (QoE) prediction model for mobile video telephony using Support Vector Regression techniques. The prediction models display ≈ 0.8 pearson correlation with experimental Multimed Tools Appl data. Our methodology and findings can be used to guide the video telephony application providers and network operators to work towards satisfying end-user experience.