The Hough Transform is an image processing technique used to detect patterns defined by analytical equations in images. The general algorithm for object detection using Hough Transform involves first detecting all the edges in the given image. The required object is then detected using and array of Accumulator cells with each cell indexed by the discretized coefficients in the analytical equation of the object to be detected. The size of the Accumulator cell array depends on the number of distinct coefficients and also the range of values that these coefficients take. The required objects generate a high value in the Accumulator cell corresponding to the coefficients using which the object can be defined. The advantage of the Hough Transform is that it can even detect discontinuous or occluded objects in images in the presence of noise. The drawback is that it has a high requirement of computing power. The multi-resolution Hough Transform (MHT) attempts to reduce this computing time. The advantage of multi-resolution analysis is that image details at various levels of coarseness can be obtained. This property is exploited to reduce the computing time in MHT. Since the resolution of the image reduces exponentially, the range of values of coefficients, and hence the Accumulator cell array size decreases logarithmically. This results in faster detection of objects.