Gaussian mixture parameter estimation with known means and unknown class-dependent variances

Pattern Recognition

Volumn 35, Issue 7, 2002, Pages 1611-1616

  Dattatreya, G R ^a  

^a The University of Texas at Dallas   (United States)

Author keywords

Adaptive receivers; Blind parameter estimation; Class dependent additive Gaussian noise; Nonuniform image quantization; Symbol dependent variances

Indexed keywords

APPROXIMATION THEORY; IMAGE PROCESSING; MARKOV PROCESSES; MIXTURES; PROBABILITY;

CONDITIONAL VARIANCES;

PATTERN RECOGNITION;

PATTERN RECOGNITION;

EID: 0036643321     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0031-3203(01)00141-8     Document Type: Article

References (7)

1. Unsupervised context estimation in a mesh of pattern classes for image recognition
2. Estimation of mixing probabilities in multiclass finite mixtures
3. Estimation of prior and transition probabilities in multiclass finite Markov mixtures
4. Parameter estimation: Known vector signals in unknown Gaussian noise
5. Mixture densities, maximum likelihood, and the EM algorithm
6. Asymptotic distribution of stochastic approximation procedures