Recent investigations on rotation invariance for 3D point clouds have been devoted to devising rotation-invariant feature descriptors or learning canonical spaces where objects are semantically aligned. Examinations of frameworks seldom looked into. In this work, we review (RI) in terms cloud registration (PCR) and propose an effective framework via three sequential stages, namely shape encodin...

Robust rotation invariance has been a matter of great interest in many applications which use low-level features such as textures. In this paper, we propose a method to analyze and capture visual patterns from textures regardless their orientation. In order to achieve rotation invariance, visual texture patterns are locally described as one-dimensional patterns by appropriately steering the Car...

ABSI'RACI' This paper concerns machine recognition of objects from their images, where the recognition is invariant to scale, translation, and rotation. A neural network used for recognizing input objects is four layer backpropagation network and a cluster of interconnected units spanning four layers of each network forms a functional block called a column. The 90" rotation invariance has been ...

This paper presents a novel algorithm, called Radial Sector Coding (RSC), for Translation, Rotation and Scale invariant character recognition. Translation invariance is obtained using Center of Mass (CoM). Scaling invariance is achieved by normalizing the features of characters. To obtain most challenging rotation invariance, RSC searches a rotation invariant Line of Reference (LoR) by exploiti...

We describe a trainable, hand-drawn symbol recognizer based on a multi-layer recognition scheme. Symbols are internally represented as binary templates. An ensemble of four template classifiers ranks each definition according to similarity with an unknown symbol. Scores from the individual classifiers are then aggregated to determine the best definition for the unknown. Ordinarily, template-mat...

Shift and rotation invariance properties of linear time-frequency representations are investigated. It is shown that among all linear time-frequency representations, only the short-time Fourier transform (STFT) family with the Hermite–Gaussian kernels satisfies both the shift invariance and rotation invariance properties that are satisfied by the Wigner distribution (WD). By extending the time-...