Studies in biological vision have always been a great source of inspiration for design of computer vision algorithms. In the past, several successful methods were designed with varying degrees of correspondence with biological vision studies, ranging from purely functional inspiration to methods that utilize models that were primarily developed for explaining biological observations. Even though it seems well recognized that computational models of visual cortex can help in design of computer vision algorithms, it is a non-trivial exercise for a computer vision researcher to mine relevant information from biological literature as very few studies in biology are organized at a task level. This has lead to a widening gap between biological vision and computer vision research. In this paper we aim to bridge this gap by provide insights and methodology to envision a new departure in this area. Not only we revisit some of the main features of biological vision and discuss the foundations of existing computational studies modeling biological vision, but also we revisit three classical computer vision tasks from a biological perspective: image sensing, segmentation and optical flow. Using this task-centric approach, we discuss well-known biological functional principles and compare them with approaches taken by computer vision. Based on this comparative analysis of computer and biological vision, we will present some recent promising approaches in modelling biological vision and we highlight a few novel ideas that we think are promising for future investigations in computer vision. To this extent, this papers provides new insights for the design of biology-based computer vision algorithms and pave a way for much needed interaction between the two communities.