Neuromorphic, or event, cameras represent a transformation in the classical approach to visual sensing encodes detected instantaneous per-pixel illumination changes into an asynchronous stream of event packets. Their novelty compared to standard cameras lies in the transition from capturing full picture frames at fixed time intervals to a sparse data format which, with its distinctive qualities, offers potential improvements in various applications. However, these advantages come at the cost of reinventing algorithmic procedures or adapting them to effectively process the new data format. In this survey, we systematically examine neuromorphic vision along three main dimensions. First, we highlight the technological evolution and distinctive hardware features of neuromorphic cameras from their inception to recent models. Second, we review image processing algorithms developed explicitly for event-based data, covering key works on feature detection, tracking, and optical flow -which form the basis for analyzing image elements and transformations -as well as depth and pose estimation or object recognition, which interpret more complex scene structures and components. These techniques, drawn from classical computer vision and modern data-driven approaches, are examined to illustrate the breadth of applications for event-based cameras. Third, we present practical application case studies demonstrating how event cameras have been successfully used across various industries and scenarios. Finally, we analyze the challenges limiting widespread adoption, identify significant research gaps compared to standard imaging techniques, and outline promising future directions and opportunities that neuromorphic vision offers.